academic research questionnaire

10 Survey Tools for Academic Research in 2024

• Important Features

Survey Panels

• Additional Tools

1. SurveyKing

2. alchemer, 3. surveymonkey, 4. qualtrics, 5. questionpro, 6. sawtooth, 7. conjointly, 8. typeform, 10. google forms.

• Employee Feedback
• Creating the Survey
• Identity Protection
• Research Tools

Need a research survey tool? Features include MaxDiff, conjoint, and more!

These ten survey tools are perfect for academic research because they offer unique question types, solid reporting options, and support staff to help make your project a success. This article includes a detailed review of each of these nine survey tools. In addition to these survey tools, we include information about other research tools and survey panels.

Below is a quick summary of these nine survey tools. We list the lowest price to upgrade, which usually has the featured s needed for research projects. We also include a summary of the unique features of each tool. Most survey software has a monthly subscription; we denote when a tool requires annual pricing is required.

Important Features of Research Survey Software

Academic research surveys often require advanced question types to capture the necessary data. Many of the tools we mention in this article include these questions. However, some projects also require specialized features or the ability to purchase a panel. To help guide your decision in choosing the best piece of software for your project, we’ll summarize some of the most critical aspects.

Research Questions

Standard multiple-choice questions can only get you so far. Here are some question types you should be aware of:

• MaxDiff – measure the relative importance of an attribute. It goes beyond a standard ranking or rating by forcing respondents to pick the least and most valued items from a list. Rankings and other types only can you what is liked, not what is disliked. A statistical model will give you the probability of a user selecting an item as the most important. Latent class analysis can help you identify groups of respondents who value different attributes.
• Conjoint – Similar to MaxDiff in terms of finding importance, respondents evaluate a complete product (multiple attributes combined). This simulates real word purchasing decisions. A statistical model is also used to compute the importance of each item.
• Van Westendorp – Asks respondents to evaluate four price points. This shapes price curves and gives you a range of acceptable prices.
• Gabor Granger – Asks users whether or not they would purchase an item at specific price points. Price points are shown in random order to simulate real-world buying conditions. The results include a demand curve, giving you the revenue-maximizing price.
• Likert Scale – Measure attitudes and opinions related to a topic. It’s essential to use a mobile-ready Likert scale tool to increase response rates; many tools use a matrix for Likert scales, which could be more user-friendly.
• Semantic differential scale – a multirow rating scale that contains grammatically opposite adjectives at each end. It is used similarly to a Likert scale but is much easier for respondents to evaluate.
• Image heat map – Respondents click on places they like on an image. The results include a heat map showing the density of clicks. This is useful for product packaging.
• Net Promoter Score – Respondents choose a rating from 0-10. Many companies use this industry-standard question to benchmark their brand perception. This question type is necessary if your academic project measures brand reputation.

Anonymous Survey Links

Many academic surveys can deal with sensitive subjects or target sensitive groups. For this reason, assuring anonymity for respondents is crucial. Choosing a platform with an anonymous link is essential to increase trust with respondents and increase your response rates.

Data Segmentation

Comparing two groups within your survey data is essential for many research projects. This is called cross tabulation . For example, consider a survey where you ask for gender along with product satisfaction. You may notice that males are not satisfied with the product while females are.

You can take this further and compute the statistical significance between the groups. In other words, make the differences that exist between two data sets due to random chance or not. Your comparison is statistically significant if it’s not due to random chance.

Some lower-end survey tools may not offer any segmentation features. If this is the case, you need to download your survey data into a spreadsheet and create pivots of set-up custom formulas.

Skip Logic and Piping

If your academic project has questions that only a specific subset of respondents need to answer, then some logic will help streamline your survey.

Skip logic will take you to a new page based on answers to previous questions. Display logic will show a question to a user based on previous questions; perfect for follow-up.

Answer piping will allow you to carry forward answers from one question into another. So, for example, ask someone which brand names they have heard of, then pipe those answers into a ranking question.

Data Cleaning

Making sure your responses are high quality is a big part of any survey research project. For example, if people speed through the survey or mark all the first answers for questions, those would be low-quality responses and should be removed from your data set. Some tools highlight these low-quality responses, which can be a helpful feature.

For platforms that do not offer a data cleaning feature, it’s generally possible to export the data to Excel, create formulas for time spent, answer straight-lining, then remove the needed data. You can also include a  trap question  to help filter out low-quality responses.

Great Support

Many academic projects require statistical analysis or additional options for the survey. Using a tool with a support staff that can explain a statistical model’s intricacies, help build custom models, or adds features on request will ensure your project is a success. With SurveyKing, custom-built features are billed at $50 per hour, making custom projects feasible for small budgets.

Asking classmates to take your survey, posting it on social media, or distributing QR code surveys around campus is a great way to collect responses for your project. But if you need more responses with those methods, purchasing additional answers might be required.

A panel provider will enable you to target a specific demographic, job role, or hobby type. When setting up a survey with a penal provider, you always want to include screening questions (on the first page) to ensure they meet your criteria, as panel filters may not be 100% accurate. Generally, panel responses start around $2.50 per completed response.  Cint  is one of the largest panel providers and works well with any survey platform.

Additional Research Tools

Before deep diving into the survey software list, here are some additional tools and resources that might assist in your project. These can help shape your survey by conducting preliminary research or using it as a substitute if conducting a study is not feasible.

• Hotjar  – They offer simple surveys and many tools to help capture feedback and data points from a website. A feedback widget customized for websites in addition to a heat map tool to show where users click the most or to identify rage clicks. A tool like this could be helpful if your academic projects revolve around launching or optimizing a website.
• Think with Google  – Used to help marketers understand their audience. The site contains links to Google Trends to search for the popularity of key terms over time. They also have a tool that helps you identify your audience based on popular YouTube channels. Finally, they have a “Grow My Store Tool” that recommends tips for improving an online store.
• Google Scholar  – A specific search engine used for scholarly literature. This can help locate research papers related to the survey you are creating.
• MIT Theses  – Contains over 58,000 theses and dissertations from all MIT departments. The database is organized by department and lets you search for keywords.

SurveyKing is the best tool for academic research surveys because of a wide variety of question types like MaxDiff, excellent reporting features, a solid support staff, and a low cost of $19 per month.

The survey builder is straightforward to use. Question types include MaxDiff, conjoint, Gabor Granger, Van Westendorp, a mobile optimized Likert scale, and semantic differential.

The MaxDiff question also includes anchored MaxDiff and collecting open-ended feedback for the feature most valued by a respondent. In addition, cluster analysis is available to help similar group data together; some respondents might value specific attributes, while other groups value others.

The reporting section is also a standout feature. It is easy to create filters and segment reports. In addition, the Excel export is well formatted easily for question types like ranking and Likert Scale, making it easy to upload into SPSS. The reporting section also gives the probability for MaxDiff, one of the few tools to offer that.

The anonymous link on SurveyKing is a valuable feature. A snippet at the top of each anonymous survey is where users can click to understand whether their identities are protected.

The software also offers a Net Promoter Score module which can come in handy for projects that deep dive into brand reputation.

Some downsides to SurveyKing include no answer piping, no image heat maps, no continuous sum question, and no premade data cleaning feature.

As a platform with lots of advanced question types and a reasonable cost, Alchemer is an excellent tool for academic research. Question types include MaxDiff, conjoint, semantic differential, image heat map, text highlighter, continuous sum, cascading dropdowns, rankings, and card grouping.

Reporting on Alchemer is a standout feature. Not only can you create filters and segment reports, but you can also create those filters and segments using advanced criteria. So if you ask a question about gender and hobby, you can make advanced criteria that match a specific gender and hobby.

In addition, their reporting section also can do chi-square tests to calculate the significant difference between the two groups. Finally, they also have a section where you can create and run your R scripts. This can be useful for various academic research projects as you can create custom statistical models in the software without needing to export your data.

Alchemer is less user-friendly than some other tools. The platform is a little clunky; things like MaxDiff require respondents to hit the submit button to get to the next set. Radio buttons need respondents to click inside of them instead of the area around them.

The pricing is reasonable for a student; $249 a month for access to the research questions. However, if you can organize your project quickly, you may only need one month of access.

As the most recognized brand for online surveys, SurveyMonkey is a reliable option for academic research. While the platform does not have any research questions, it offers all the standard question types and a clean user interface to build your surveys.

One advanced question type they do have is the image heat map. Their parent company  Momentive  does offer things like MaxDiff and conjoint studies, but you would need to contact sales to get a quote, meaning this could be out of budget for students.

The reporting on SurveyMonkey is good. You can easily create filters and segments. You can also save that criterion to create a view. The views enable you to toggle between rules quickly.

One of the main downsides to SurveyMonkey is the cost. For the image heat map and to create advanced branching rules, you need to upgrade to their Premier plan, which costs $1,428 annually. To get statistical significance, you would need their Primer plan, which is $468 annually.

As the survey tool known for experience management, Qualtrics has some nice features for research projects. For example, they offer both MaxDiff and conjoint in addition to tools like drill-down, continuous sun, image heat map, and a text highlighter.

Reporting on the tool offers the ability to create filters and segments. For segments, it’s called a report breakout, and it appears there is no ability to create a breakout with advanced criteria. However, filers do allow you for advanced criteria.

There is a custom report builder option to create custom PDF reports. You can add as many elements as needed and customize the information displayed, whether a chart type or a data table.

Overall, Qualtrics could be more user-friendly and may require training. The survey builder and reporting screens could be more cohesive. For example, to add more answer options, you need to click the “plus” symbol on the left-hand side of the question instead of just hitting enter or clicking a button right below the current answer choice. In addition, the reporting section will display things like mean and standard deviation for simple multiple-choice questions before showing simple response counts.

One drawback to Qualtrics is the pricing. For example, you would need to pay $1,440 for an annual plan to use the research questions. But many universities have a licensing agreement with Qualtrics so students can use the platform. When you sign up for a new account, you can select academic use, enter your Edu email, and they will check if your university has a license agreement.

A survey platform with all the needed research questions, including Gabor Granger and Van Westendorp, QuestionPro is a quality research tool.

The reporting on QuestionPro is comprehensive. They offer segment reports with statistical significance using a t-test. In addition, they offer TURF analysis to show answer combinations with the highest reach.

For conjoint, offer a market simulation tool that can forecast new product market share based on your data. That tool can also calculate how much  premium  consumers will pay for a brand name.

QuestionPro is a little easier to use than Qualtrics. The UI is cleaner but still clumsy. You must navigate to a different section in the builder for things like quotas instead of just having it near skip logic rules. The distribution page has the link at the top but an email body below. The reporting has a lot of different pages to click through for each option. Small things like this mean there is a learning curve to use the platform efficiently.

The biggest downside of QuestionPro is the price. All of their research questions, even Net Promoter Score, would require a custom quote under the research plan. There another plan with upgraded feature types is $1,188 annually.

When it comes to advanced research projects, Sawtooth is a great resource. While their survey builder is a little limited in question types, they offer different forms of MaxDiff and conjoint. They also provide consulting services, which could help if your academic project is highly specialized.

For MaxDiff, they offer a bandit  version, which can be used for MaxDiff studies with over 50 attributes. Each set of detailed attributes that are most relevant to the user. This can save panel costs because you can build a suitable statistical model with 300 bandit responses compared with 500 or 1000 standard MaxDiff responses.

Their MaxDiff feature also comes with a TURF analysis option that can show you the possible market research of various attributes.

For conjoint, they offer adaptive choice-based conjoint and menu-based conjoint. Adaptive choice tailors the product cards toward each respondent based on early responses or screening questions. Menu-based conjoint is for more complex projects, allowing respondents to build their products based on various attributes and prices.

Sawtooth has a high price point and may be out of the research for many academic projects. The lowest plan is $4,500 annually. If you need advanced tools like bandit MaxDiff or adaptive conjoint, you must pay $11,990 annually. They do have a package just for MaxDiff starting at $2,420.

Conjointly is a platform geared towards research projects, namely market research. Not only do they have the standard research questions, but they also have a bunch of unique ones: claims testing, Kano Model testing, and monadic testing. There are also question types like feature placement matrix, which combines MaxDiff and Gabor Granger into a single question.

You can either use your respondents or select from a survey panel. The survey panel option comes with predefined audiences, which makes scouring respondents a breeze.

One unique feature is that they monitor in real-time speeders and other criteria for low-quality respondents. If a respondent is speeding through the survey, a warning message is displayed asking them to repeat questions before being disqualified. If a question has a lot of information to digest, the system automatically pauses, forcing the respondent to thoroughly read the question before answering.

The pricing is a little steep at $1,795 annually. Response panels for USA residents appear to start around $4 per completed response. The survey builder and reporting section could be cleaner, with different options in many places. It may take time to get up to speed.

While Typeform doesn’t have any research questions, it is a very well-designed and easy-to-use tool that can assist with your academic survey. For example, it could gather preliminary data for a MaxDiff study.

Typeform offers a lot of integrations with other applications. For example, if your project requires exporting data to a spreadsheet, then Google Sheets or Excel integration might be helpful. Likewise, if your research project is part of a class project, then the Slack or Microsoft Teams integration might help to notify other team members when you get responses.

One unique feature of Typeform is the calculator feature. Add, subtract, and multiply numbers to the @score or @price variable. These variables can be recalled to show scores or used in a payment form.

The reporting in Typeform is basic. There is no option to create a filter or a segment report. Any data analysis would need to be done in Google Sheets or Excel.

For $29 a month, you can get 100 responses, or $59 a month, you can collect 1,000 responses each month.

HubSpot’s form builder doesn’t include advanced research questions, but it is customizable and easy to use. You can pick between multiple form types, like standalone, embedded, and pop-up forms. There are also numerous templates including lead generation, support, or eBook download forms.

One of the form builder’s main advantages is that it offers native integrations with Salesforce and HubSpot CRM tools. In addition, you can add custom properties to the form from the CRM fields. You can then send the surveys in bulk to your audience using their email features.

If you’re a large academic organization. HubSpot would be ideal to organize and manage a large distribution list. If you need advanced questions, you can still use HubSpot for an initial screener survey or incorporate other surveys into HubSpot using skip logic.

As for the downsides, scalability can be challenging. Although you can use the form builder for free, you’ll have to subscribe to one of HubSpot’s Marketing Hub paid plans to access more advanced functionalities, like unlimited automation workflows and code customization. There are steep pricing differences between paid packages. 

One of the widely used survey tools, Google Forms , is a decent platform for an academic research survey. Unfortunately, the software doesn’t offer any research questions. Still, the few questions it has, like multiple choice, rantings, and open-ended feedback, are enough to collect essential feedback for simple projects or preliminary data for more complex studies.

Skip logic is straightforward to set up on Google Forms. For example, you can select what section to skip based on question answers or choose what to skip once a section is complete. Of course, you can’t create complex rules, but these simple rules can cover many bases.

Overall the user interface is elegant and straightforward. The form design is also elegant, meaning the respondent experience is excellent. Unlike other survey tools, which can have a clunky interface, there is no worry about that with Google Forms; respondents can quickly navigate your form and submit answers.

The spreadsheet export is very well formatted and can be easily imported into SPSS for advanced analysis. However, the export has the submission date and time but has yet to have the time started, so calculating speeders is impossible.

ABOUT THE AUTOR

Allen is the founder of SurveyKing. A former CPA and government auditor, he understands how important quality data is in decision making. He continues to help SurveyKing accomplish their main goal: providing organizations around the world with low-cost high-quality feedback tools.

Ready To Start?

Create your own survey now. Get started for free and collect actionable data.

10 Great SurveyMonkey Alternatives to Use in 2024

Discover alternatives to the most popular online survey tool, SurveyMonkey. Gain an understanding of where SurveyMonkey lacks in features and get intr...

8 Best Strategies of Survey Promotion to Increase Response Rates

Survey promotion is the process of spreading surveys through different channels to encourage more responses and participation. It involves placing the...

Business Process Improvement Consulting: Expert Solutions

Definition: A business process improvement consultant will help design and implement strategies to increase the efficiency of workflows across your o...

8 Excel Consulting Services to Use in 2024 + VBA Support

These 6 Excel consulting firms offer support, training, and VBA development to help you automate tasks and increase efficiency when using Microsoft Ex...

7 Great Qualtrics Alternatives to Use in 2024

These seven alternatives to Qualtrics offer either more features, a lower cost, or a cleaner user interface. These alternative platforms also include ...

Union Negotiation Consulting: Planning Labor Agreements

A labor union negotiation consulting engagement involves quantifying member needs, proposing contract language, and developing communication strategie...

Creating a Transactional Survey: Examples + Template

Definition: A transactional survey captures customer feedback after a specific interaction, referred to as a touchpoint. This survey type provides dir...

Hire an Excel Expert: Automation + VBA Development

An Excel expert will help you to complete your projects within Microsoft Excel. A good Excel expert should be proficient in advanced formulas such as ...

Creating an Anonymous Employee Survey + Template, Sample Questions

Definition: An anonymous employee survey is a convenient way to collect honest feedback in the workplace. The survey can either measure employee satis...

Improving Fleet Performance Through Driver Feedback Surveys

In the US, the trucking industry generated $875.5 billion in gross freight revenues, accounting for 80.8% of the country’s freight invoice in 20...

13 College Study Tips to Use in 2023

These 15 college study tips will help you succeed in your academic career.

Maximizing the Value of Skills Assessment Tools Using Surveys

When you apply for a job, it’s only natural that you’ll aim to present the best possible version of yourself. You’ll focus on your best skills a...

Creating UX Surveys: 6 Tips and Examples

UX surveys are used to help create a great user experience. A good UX survey will incorporate a variety of question types to help understand what user...

5 Web Consultants to Use in 2023: Design + Development

Definition: A web consultant can update an existing website design, create a custom website, help increase traffic, recommend layout changes, and even...

Creating a Targeted Survey: Panels to Reach Your Audience

Definition: A targeted survey is used to research a specific audience, frequently utilizing a survey panel provider. A paneling service generally has ...

8 Typeform Alternatives to Use in 2023

These seven alternatives to Typeform offer a lower cost or additional features. In addition, these alternative platforms include question types that T...

7 Ecommerce Skills For Professionals + Students

Ecommerce has occupied its leading niche in the world, allowing us to draw certain conclusions. For example, it is not surprising that more specialize...

Ecommerce Analytics Explained + Tools to Use

Definition: Ecommerce analytics is the practice of continuously monitoring your business performance by gathering and examining data that affects your...

Planning a Survey: 6 Step Guide + Best Practices

Planning a survey involves six steps: Set objectives, define the target audience, select the distribution method, organize external data, draft the su...

4 Survey Consulting Services to Use in 2023

Definition: These 4 survey consulting services offer planning, design, development, and support to help complete your survey project. Whether it’s f...

Excel Automation Explained: VBA Code + Sample Workbooks

Definition: Excel automation will streamline repetitive tasks such as updating data, formatting cells, sending emails, and even uploading files to Sha...

Hire a Financial Modeling Consultant: Forecasts + Valuations

Definition: A financial modeling consultant will provide expertise in planning budgets, generating forecasts, creating valuations, and providing equit...

Excel Programming Services: Development, Macros, VBA

Definition: An excel programmer can be hired to organize workbooks, create custom formulas, automate repetitive tasks using VBA, and can consult on h...

Market Research Surveys: Sample Questions + Template

Definition: Market research surveys are a tool used to collect information about a target market. These surveys allow businesses to understand market ...

What do Americans Value Most in the Coming Election? A Comprehensive and Interactive 2020 Voter Poll

SurveyKing set out on a mission in the fall of 2020, to poll American's and help identify, with quantifiable data, what issues american are most focus...

Get Started Now

We have you covered on anything from customer surveys, employee surveys, to market research. Get started and create your first survey for free.

• Utility Menu

Harvard University Program on Survey Research

• Questionnaire Design Tip Sheet

This PSR Tip Sheet provides some basic tips about how to write good survey questions and design a good survey questionnaire.

PSR Resources

• Managing and Manipulating Survey Data: A Beginners Guide
• Finding and Hiring Survey Contractors
• How to Frame and Explain the Survey Data Used in a Thesis
• Overview of Cognitive Testing and Questionnaire Evaluation
• Sampling, Coverage, and Nonresponse Tip Sheet
• Introduction to Surveys for Honors Thesis Writers
• PSR Introduction to the Survey Process
• Related Centers/Programs at Harvard
• General Survey Reference
• Institutional Review Boards
• Select Funding Opportunities
• Survey Analysis Software
• Professional Standards
• Professional Organizations
• Major Public Polls
• Survey Data Collections
• Major Longitudinal Surveys
• Other Links

Academic survey questions, examples, and a step-by-step guide

Use SurveyPlanet for your academic research and gather real data that supports your thesis.

If you want to deepen your knowledge about a particular subject by testing theories, then academic surveys are invaluable. Coming up with a hypothesis is hard enough and a well-designed survey—with carefully constructed questions—will help you test how viable your hypothesis actually is.

The best survey tool for academic research

SurveyPlanet is a great tool for creating academic surveys that will let you put theoretical knowledge into practice and learn by doing. With dozens of templates that include pre-written questions, you will learn right away what a great academic survey should look like.

You’ll also find powerful features like question branching, survey-length estimation, four chart types to display results, and many more when you sign up for a SurveyPlanet account to create an academic survey. Our user-friendly designs will make both creating and filling out surveys a simple yet exciting experience for both you and your respondents.

A step-by-step guide to creating a successful academic survey

• First, gather your thoughts by putting them on paper in order to create a strategy for a successful academic survey.
• Set one ultimate goal, then divide this into smaller, actionable steps that will lead to achieving it.
• Consider your hypothesis and figure out how an academic survey will help you confirm it.
• Before creating questions, decide or learn who will be your target group.
• After you know your target group, think about how big your sample should be to produce statistically significant data. You can do that with our free survey sample size calculator.
• Whether you need one hundred or one thousand responses, think about where your target sample hangs out and how you’ll distribute the survey. Share a survey link with different communities on social media and reach out to friends and acquaintances. Maybe they aren’t your target group, but a friend of a friend is. Don’t get discouraged, people love to help with academic research.
• Books are one thing, real-life data is another. Think about implementing what you already know in your academic survey and how that knowledge can serve a higher purpose.
• When writing questions make sure to use different types, such as multiple choice, Likert scale, open-ended, image choice, and ranking questions. This will make your survey more engaging and fewer people will drop out because they didn't make it through to the end.
• Be brief and concise, both with questions and the survey itself. Think about whether some questions are really necessary. Write questions with straightforward language.
• Make sure your survey isn’t too long. That will put respondents off. With a survey length estimate , you don’t have to manually estimate the length, since SurveyPlanet can do that for you.
• Creating an academic survey and gathering data is great, but you also need to analyze the results. Figure out which method you’ll use before distributing the survey and analyze the quantitative data first (because it’s more straightforward).

While we can't promise that, with the help of our survey tools, you will attain academic excellence in no time, we are sure that our tools will provide a valuable service in your research endeavors. In fact, our mission is to facilitate all the stages of conducting research , from ideation to analysis. That is why we made guidelines covering every step of the process, from creating a survey to survey data analysis for better insights.

Creating your academic survey online is one of the least expensive—but effective—ways to gather all the data you need. People are more eager to respond to an online survey in their free time, from the comfort of their homes. With SurveyPlanet as your partner, you will garner a high response rate and much useful data.

Academic surveys questions and examples

The quality of questions directly influences the quality of data. At the end of the day, it’s the quality of results that matter. Because of that, we pay special attention to creating academic survey questions that are useful for both students and respondents. Academic surveys usually require some demographic questions , including:

• Please select your age range:
• 18 or younger
• Please select your gender:
• What’s your marital status?
• What’s the highest level of education you’ve completed?
• Less than high school
• High school
• Bachelor degree
• Masters degree
• Which category best describes your employment status?
• Employed full-time (40 hours a week or more)
• Employed part-time (less than 40 hours a week)
• Which category best fits the yearly household income of every member combined?
• $20,000-$59,999
• $60,000-$99,999
• $100,000-$149,999
• $150,000 or more

Academic surveys can explore and research many different topics. It just depends on what your area of interest is. Here are some academic survey examples to give you a better idea:

Healthcare surveys

With healthcare surveys, you can research patient demographics, figure out how accessible healthcare is, some common issues people encounter, and ways to improve performance.

Read more about healthcare surveys

Education surveys

Student satisfaction is a topic for which there is always more to ask and say. Using education surveys, you can explore students’ habits, assess the quality of their education, and research teachers’ working conditions.

Read more about education surveys

From employee satisfaction to work-life balance, HR surveys are an inexhaustible source for researching and studying workplace issues.

Read more about HR surveys

Brand surveys

Research consumer demographics and how they perceive different brands to draw conclusions based on the data you collect.

Read more about brand surveys

Depending on the theme that is being addressed, this type of questionnaire can be labeled as a:

• postgraduate taught experience survey
• academic performance survey
• academic research survey

Specific questions depend on the subject you’re studying and researching and we have dozens of academic survey templates to choose from.

SurveyPlanet can help you with your academic research. Use our survey tools, which can help you gather valuable data and insights in no time while providing the best experience to your examinees. Just sign up for a SurveyPlanet account and create an academic survey that will be ready for responses.

Sign up now

Free unlimited surveys, questions and responses.

How to Design Effective Research Questionnaires for Robust Findings

As a staple in data collection, questionnaires help uncover robust and reliable findings that can transform industries, shape policies, and revolutionize understanding. Whether you are exploring societal trends or delving into scientific phenomena, the effectiveness of your research questionnaire can make or break your findings.

In this article, we aim to understand the core purpose of questionnaires, exploring how they serve as essential tools for gathering systematic data, both qualitative and quantitative, from diverse respondents. Read on as we explore the key elements that make up a winning questionnaire, the art of framing questions which are both compelling and rigorous, and the careful balance between simplicity and depth.

Table of Contents

The Role of Questionnaires in Research

So, what is a questionnaire? A questionnaire is a structured set of questions designed to collect information, opinions, attitudes, or behaviors from respondents. It is one of the most commonly used data collection methods in research. Moreover, questionnaires can be used in various research fields, including social sciences, market research, healthcare, education, and psychology. Their adaptability makes them suitable for investigating diverse research questions.

Questionnaire and survey  are two terms often used interchangeably, but they have distinct meanings in the context of research. A survey refers to the broader process of data collection that may involve various methods. A survey can encompass different data collection techniques, such as interviews , focus groups, observations, and yes, questionnaires.

Pros and Cons of Using Questionnaires in Research:

While questionnaires offer numerous advantages in research, they also come with some disadvantages that researchers must be aware of and address appropriately. Careful questionnaire design, validation, and consideration of potential biases can help mitigate these disadvantages and enhance the effectiveness of using questionnaires as a data collection method.

Structured vs Unstructured Questionnaires

Structured questionnaire:.

A structured questionnaire consists of questions with predefined response options. Respondents are presented with a fixed set of choices and are required to select from those options. The questions in a structured questionnaire are designed to elicit specific and quantifiable responses. Structured questionnaires are particularly useful for collecting quantitative data and are often employed in surveys and studies where standardized and comparable data are necessary.

Advantages of Structured Questionnaires:

• Easy to analyze and interpret: The fixed response options facilitate straightforward data analysis and comparison across respondents.
• Efficient for large-scale data collection: Structured questionnaires are time-efficient, allowing researchers to collect data from a large number of respondents.
• Reduces response bias: The predefined response options minimize potential response bias and maintain consistency in data collection.

Limitations of Structured Questionnaires:

• Lack of depth: Structured questionnaires may not capture in-depth insights or nuances as respondents are limited to pre-defined response choices. Hence, they may not reveal the reasons behind respondents’ choices, limiting the understanding of their perspectives.
• Limited flexibility: The fixed response options may not cover all potential responses, therefore, potentially restricting respondents’ answers.

Unstructured Questionnaire:

An unstructured questionnaire consists of questions that allow respondents to provide detailed and unrestricted responses. Unlike structured questionnaires, there are no predefined response options, giving respondents the freedom to express their thoughts in their own words. Furthermore, unstructured questionnaires are valuable for collecting qualitative data and obtaining in-depth insights into respondents’ experiences, opinions, or feelings.

Advantages of Unstructured Questionnaires:

• Rich qualitative data: Unstructured questionnaires yield detailed and comprehensive qualitative data, providing valuable and novel insights into respondents’ perspectives.
• Flexibility in responses: Respondents have the freedom to express themselves in their own words. Hence, allowing for a wide range of responses.

Limitations of Unstructured Questionnaires:

• Time-consuming analysis: Analyzing open-ended responses can be time-consuming, since, each response requires careful reading and interpretation.
• Subjectivity in interpretation: The analysis of open-ended responses may be subjective, as researchers interpret and categorize responses based on their judgment.
• May require smaller sample size: Due to the depth of responses, researchers may need a smaller sample size for comprehensive analysis, making generalizations more challenging.

Types of Questions in a Questionnaire

In a questionnaire, researchers typically use the following most common types of questions to gather a variety of information from respondents:

1. Open-Ended Questions:

These questions allow respondents to provide detailed and unrestricted responses in their own words. Open-ended questions are valuable for gathering qualitative data and in-depth insights.

Example: What suggestions do you have for improving our product?

2. Multiple-Choice Questions

Respondents choose one answer from a list of provided options. This type of question is suitable for gathering categorical data or preferences.

Example: Which of the following social media/academic networking platforms do you use to promote your research?

• ResearchGate
• Academia.edu

3. Dichotomous Questions

Respondents choose between two options, typically “yes” or “no”, “true” or “false”, or “agree” or “disagree”.

Example: Have you ever published in open access journals before?

4. Scaling Questions

These questions, also known as rating scale questions, use a predefined scale that allows respondents to rate or rank their level of agreement, satisfaction, importance, or other subjective assessments. These scales help researchers quantify subjective data and make comparisons across respondents.

There are several types of scaling techniques used in scaling questions:

i. Likert Scale:

The Likert scale is one of the most common scaling techniques. It presents respondents with a series of statements and asks them to rate their level of agreement or disagreement using a range of options, typically from “strongly agree” to “strongly disagree”.For example: Please indicate your level of agreement with the statement: “The content presented in the webinar was relevant and aligned with the advertised topic.”

• Strongly Agree
• Strongly Disagree

ii. Semantic Differential Scale:

The semantic differential scale measures respondents’ perceptions or attitudes towards an item using opposite adjectives or bipolar words. Respondents rate the item on a scale between the two opposites. For example:

• Easy —— Difficult
• Satisfied —— Unsatisfied
• Very likely —— Very unlikely

iii. Numerical Rating Scale:

This scale requires respondents to provide a numerical rating on a predefined scale. It can be a simple 1 to 5 or 1 to 10 scale, where higher numbers indicate higher agreement, satisfaction, or importance.

iv. Ranking Questions:

Respondents rank items in order of preference or importance. Ranking questions help identify preferences or priorities.

Example: Please rank the following features of our app in order of importance (1 = Most Important, 5 = Least Important):

• User Interface
• Functionality
• Customer Support

By using a mix of question types, researchers can gather both quantitative and qualitative data, providing a comprehensive understanding of the research topic and enabling meaningful analysis and interpretation of the results. The choice of question types depends on the research objectives , the desired depth of information, and the data analysis requirements.

Methods of Administering Questionnaires

There are several methods for administering questionnaires, and the choice of method depends on factors such as the target population, research objectives , convenience, and resources available. Here are some common methods of administering questionnaires:

Each method has its advantages and limitations. Online surveys offer convenience and a large reach, but they may be limited to individuals with internet access. Face-to-face interviews allow for in-depth responses but can be time-consuming and costly. Telephone surveys have broad reach but may be limited by declining response rates. Researchers should choose the method that best suits their research objectives, target population, and available resources to ensure successful data collection.

How to Design a Questionnaire

Designing a good questionnaire is crucial for gathering accurate and meaningful data that aligns with your research objectives. Here are essential steps and tips to create a well-designed questionnaire:

1. Define Your Research Objectives : Clearly outline the purpose and specific information you aim to gather through the questionnaire.

2. Identify Your Target Audience : Understand respondents’ characteristics and tailor the questionnaire accordingly.

3. Develop the Questions :

• Write Clear and Concise Questions
• Avoid Leading or Biasing Questions
• Sequence Questions Logically
• Group Related Questions
• Include Demographic Questions

4. Provide Well-defined Response Options : Offer exhaustive response choices for closed-ended questions.

5. Consider Skip Logic and Branching : Customize the questionnaire based on previous answers.

6. Pilot Test the Questionnaire : Identify and address issues through a pilot study .

7. Seek Expert Feedback : Validate the questionnaire with subject matter experts.

8. Obtain Ethical Approval : Comply with ethical guidelines , obtain consent, and ensure confidentiality before administering the questionnaire.

9. Administer the Questionnaire : Choose the right mode and provide clear instructions.

10. Test the Survey Platform : Ensure compatibility and usability for online surveys.

By following these steps and paying attention to questionnaire design principles, you can create a well-structured and effective questionnaire that gathers reliable data and helps you achieve your research objectives.

Characteristics of a Good Questionnaire

A good questionnaire possesses several essential elements that contribute to its effectiveness. Furthermore, these characteristics ensure that the questionnaire is well-designed, easy to understand, and capable of providing valuable insights. Here are some key characteristics of a good questionnaire:

1. Clarity and Simplicity : Questions should be clear, concise, and unambiguous. Avoid using complex language or technical terms that may confuse respondents. Simple and straightforward questions ensure that respondents interpret them consistently.

2. Relevance and Focus : Each question should directly relate to the research objectives and contribute to answering the research questions. Consequently, avoid including extraneous or irrelevant questions that could lead to data clutter.

3. Mix of Question Types : Utilize a mix of question types, including open-ended, Likert scale, and multiple-choice questions. This variety allows for both qualitative and quantitative data collections .

4. Validity and Reliability : Ensure the questionnaire measures what it intends to measure (validity) and produces consistent results upon repeated administration (reliability). Validation should be conducted through expert review and previous research.

5. Appropriate Length : Keep the questionnaire’s length appropriate and manageable to avoid respondent fatigue or dropouts. Long questionnaires may result in incomplete or rushed responses.

6. Clear Instructions : Include clear instructions at the beginning of the questionnaire to guide respondents on how to complete it. Explain any technical terms, formats, or concepts if necessary.

7. User-Friendly Format : Design the questionnaire to be visually appealing and user-friendly. Use consistent formatting, adequate spacing, and a logical page layout.

8. Data Validation and Cleaning : Incorporate validation checks to ensure data accuracy and reliability. Consider mechanisms to detect and correct inconsistent or missing responses during data cleaning.

By incorporating these characteristics, researchers can create a questionnaire that maximizes data quality, minimizes response bias, and provides valuable insights for their research.

In the pursuit of advancing research and gaining meaningful insights, investing time and effort into designing effective questionnaires is a crucial step. A well-designed questionnaire is more than a mere set of questions; it is a masterpiece of precision and ingenuity. Each question plays a vital role in shaping the narrative of our research, guiding us through the labyrinth of data to meaningful conclusions. Indeed, a well-designed questionnaire serves as a powerful tool for unlocking valuable insights and generating robust findings that impact society positively.

Have you ever designed a research questionnaire? Reflect on your experience and share your insights with researchers globally through Enago Academy’s Open Blogging Platform . Join our diverse community of 1000K+ researchers and authors to exchange ideas, strategies, and best practices, and together, let’s shape the future of data collection and maximize the impact of questionnaires in the ever-evolving landscape of research.

Frequently Asked Questions

A research questionnaire is a structured tool used to gather data from participants in a systematic manner. It consists of a series of carefully crafted questions designed to collect specific information related to a research study.

Questionnaires play a pivotal role in both quantitative and qualitative research, enabling researchers to collect insights, opinions, attitudes, or behaviors from respondents. This aids in hypothesis testing, understanding, and informed decision-making, ensuring consistency, efficiency, and facilitating comparisons.

Questionnaires are a versatile tool employed in various research designs to gather data efficiently and comprehensively. They find extensive use in both quantitative and qualitative research methodologies, making them a fundamental component of research across disciplines. Some research designs that commonly utilize questionnaires include: a) Cross-Sectional Studies b) Longitudinal Studies c) Descriptive Research d) Correlational Studies e) Causal-Comparative Studies f) Experimental Research g) Survey Research h) Case Studies i) Exploratory Research

A survey is a comprehensive data collection method that can include various techniques like interviews and observations. A questionnaire is a specific set of structured questions within a survey designed to gather standardized responses. While a survey is a broader approach, a questionnaire is a focused tool for collecting specific data.

The choice of questionnaire type depends on the research objectives, the type of data required, and the preferences of respondents. Some common types include: • Structured Questionnaires: These questionnaires consist of predefined, closed-ended questions with fixed response options. They are easy to analyze and suitable for quantitative research. • Semi-Structured Questionnaires: These questionnaires combine closed-ended questions with open-ended ones. They offer more flexibility for respondents to provide detailed explanations. • Unstructured Questionnaires: These questionnaires contain open-ended questions only, allowing respondents to express their thoughts and opinions freely. They are commonly used in qualitative research.

Following these steps ensures effective questionnaire administration for reliable data collection: • Choose a Method: Decide on online, face-to-face, mail, or phone administration. • Online Surveys: Use platforms like SurveyMonkey • Pilot Test: Test on a small group before full deployment • Clear Instructions: Provide concise guidelines • Follow-Up: Send reminders if needed

Thank you, Riya. This is quite helpful. As discussed, response bias is one of the disadvantages in the use of questionnaires. One way to help limit this can be to use scenario based questions. These type of questions may help the respondents to be more reflective and active in the process.

Thank you, Dear Riya. This is quite helpful.

Great insights there Doc

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

Ensuring Academic Integrity and Transparency in Academic Research: A comprehensive checklist for researchers

Academic integrity is the foundation upon which the credibility and value of scientific findings are…

• Publishing Research
• Reporting Research

How to Optimize Your Research Process: A step-by-step guide

For researchers across disciplines, the path to uncovering novel findings and insights is often filled…

• Industry News
• Trending Now

Breaking Barriers: Sony and Nature unveil “Women in Technology Award”

Sony Group Corporation and the prestigious scientific journal Nature have collaborated to launch the inaugural…

Achieving Research Excellence: Checklist for good research practices

Academia is built on the foundation of trustworthy and high-quality research, supported by the pillars…

• Promoting Research

Plain Language Summary — Communicating your research to bridge the academic-lay gap

Science can be complex, but does that mean it should not be accessible to the…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

What would be most effective in reducing research misconduct?

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Adv Pract Oncol
• v.6(2); Mar-Apr 2015

Understanding and Evaluating Survey Research

A variety of methodologic approaches exist for individuals interested in conducting research. Selection of a research approach depends on a number of factors, including the purpose of the research, the type of research questions to be answered, and the availability of resources. The purpose of this article is to describe survey research as one approach to the conduct of research so that the reader can critically evaluate the appropriateness of the conclusions from studies employing survey research.

SURVEY RESEARCH

Survey research is defined as "the collection of information from a sample of individuals through their responses to questions" ( Check & Schutt, 2012, p. 160 ). This type of research allows for a variety of methods to recruit participants, collect data, and utilize various methods of instrumentation. Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behavior, surveys are therefore frequently used in social and psychological research ( Singleton & Straits, 2009 ).

Information has been obtained from individuals and groups through the use of survey research for decades. It can range from asking a few targeted questions of individuals on a street corner to obtain information related to behaviors and preferences, to a more rigorous study using multiple valid and reliable instruments. Common examples of less rigorous surveys include marketing or political surveys of consumer patterns and public opinion polls.

Survey research has historically included large population-based data collection. The primary purpose of this type of survey research was to obtain information describing characteristics of a large sample of individuals of interest relatively quickly. Large census surveys obtaining information reflecting demographic and personal characteristics and consumer feedback surveys are prime examples. These surveys were often provided through the mail and were intended to describe demographic characteristics of individuals or obtain opinions on which to base programs or products for a population or group.

More recently, survey research has developed into a rigorous approach to research, with scientifically tested strategies detailing who to include (representative sample), what and how to distribute (survey method), and when to initiate the survey and follow up with nonresponders (reducing nonresponse error), in order to ensure a high-quality research process and outcome. Currently, the term "survey" can reflect a range of research aims, sampling and recruitment strategies, data collection instruments, and methods of survey administration.

Given this range of options in the conduct of survey research, it is imperative for the consumer/reader of survey research to understand the potential for bias in survey research as well as the tested techniques for reducing bias, in order to draw appropriate conclusions about the information reported in this manner. Common types of error in research, along with the sources of error and strategies for reducing error as described throughout this article, are summarized in the Table .

Sources of Error in Survey Research and Strategies to Reduce Error

The goal of sampling strategies in survey research is to obtain a sufficient sample that is representative of the population of interest. It is often not feasible to collect data from an entire population of interest (e.g., all individuals with lung cancer); therefore, a subset of the population or sample is used to estimate the population responses (e.g., individuals with lung cancer currently receiving treatment). A large random sample increases the likelihood that the responses from the sample will accurately reflect the entire population. In order to accurately draw conclusions about the population, the sample must include individuals with characteristics similar to the population.

It is therefore necessary to correctly identify the population of interest (e.g., individuals with lung cancer currently receiving treatment vs. all individuals with lung cancer). The sample will ideally include individuals who reflect the intended population in terms of all characteristics of the population (e.g., sex, socioeconomic characteristics, symptom experience) and contain a similar distribution of individuals with those characteristics. As discussed by Mady Stovall beginning on page 162, Fujimori et al. ( 2014 ), for example, were interested in the population of oncologists. The authors obtained a sample of oncologists from two hospitals in Japan. These participants may or may not have similar characteristics to all oncologists in Japan.

Participant recruitment strategies can affect the adequacy and representativeness of the sample obtained. Using diverse recruitment strategies can help improve the size of the sample and help ensure adequate coverage of the intended population. For example, if a survey researcher intends to obtain a sample of individuals with breast cancer representative of all individuals with breast cancer in the United States, the researcher would want to use recruitment strategies that would recruit both women and men, individuals from rural and urban settings, individuals receiving and not receiving active treatment, and so on. Because of the difficulty in obtaining samples representative of a large population, researchers may focus the population of interest to a subset of individuals (e.g., women with stage III or IV breast cancer). Large census surveys require extremely large samples to adequately represent the characteristics of the population because they are intended to represent the entire population.

DATA COLLECTION METHODS

Survey research may use a variety of data collection methods with the most common being questionnaires and interviews. Questionnaires may be self-administered or administered by a professional, may be administered individually or in a group, and typically include a series of items reflecting the research aims. Questionnaires may include demographic questions in addition to valid and reliable research instruments ( Costanzo, Stawski, Ryff, Coe, & Almeida, 2012 ; DuBenske et al., 2014 ; Ponto, Ellington, Mellon, & Beck, 2010 ). It is helpful to the reader when authors describe the contents of the survey questionnaire so that the reader can interpret and evaluate the potential for errors of validity (e.g., items or instruments that do not measure what they are intended to measure) and reliability (e.g., items or instruments that do not measure a construct consistently). Helpful examples of articles that describe the survey instruments exist in the literature ( Buerhaus et al., 2012 ).

Questionnaires may be in paper form and mailed to participants, delivered in an electronic format via email or an Internet-based program such as SurveyMonkey, or a combination of both, giving the participant the option to choose which method is preferred ( Ponto et al., 2010 ). Using a combination of methods of survey administration can help to ensure better sample coverage (i.e., all individuals in the population having a chance of inclusion in the sample) therefore reducing coverage error ( Dillman, Smyth, & Christian, 2014 ; Singleton & Straits, 2009 ). For example, if a researcher were to only use an Internet-delivered questionnaire, individuals without access to a computer would be excluded from participation. Self-administered mailed, group, or Internet-based questionnaires are relatively low cost and practical for a large sample ( Check & Schutt, 2012 ).

Dillman et al. ( 2014 ) have described and tested a tailored design method for survey research. Improving the visual appeal and graphics of surveys by using a font size appropriate for the respondents, ordering items logically without creating unintended response bias, and arranging items clearly on each page can increase the response rate to electronic questionnaires. Attending to these and other issues in electronic questionnaires can help reduce measurement error (i.e., lack of validity or reliability) and help ensure a better response rate.

Conducting interviews is another approach to data collection used in survey research. Interviews may be conducted by phone, computer, or in person and have the benefit of visually identifying the nonverbal response(s) of the interviewee and subsequently being able to clarify the intended question. An interviewer can use probing comments to obtain more information about a question or topic and can request clarification of an unclear response ( Singleton & Straits, 2009 ). Interviews can be costly and time intensive, and therefore are relatively impractical for large samples.

Some authors advocate for using mixed methods for survey research when no one method is adequate to address the planned research aims, to reduce the potential for measurement and non-response error, and to better tailor the study methods to the intended sample ( Dillman et al., 2014 ; Singleton & Straits, 2009 ). For example, a mixed methods survey research approach may begin with distributing a questionnaire and following up with telephone interviews to clarify unclear survey responses ( Singleton & Straits, 2009 ). Mixed methods might also be used when visual or auditory deficits preclude an individual from completing a questionnaire or participating in an interview.

FUJIMORI ET AL.: SURVEY RESEARCH

Fujimori et al. ( 2014 ) described the use of survey research in a study of the effect of communication skills training for oncologists on oncologist and patient outcomes (e.g., oncologist’s performance and confidence and patient’s distress, satisfaction, and trust). A sample of 30 oncologists from two hospitals was obtained and though the authors provided a power analysis concluding an adequate number of oncologist participants to detect differences between baseline and follow-up scores, the conclusions of the study may not be generalizable to a broader population of oncologists. Oncologists were randomized to either an intervention group (i.e., communication skills training) or a control group (i.e., no training).

Fujimori et al. ( 2014 ) chose a quantitative approach to collect data from oncologist and patient participants regarding the study outcome variables. Self-report numeric ratings were used to measure oncologist confidence and patient distress, satisfaction, and trust. Oncologist confidence was measured using two instruments each using 10-point Likert rating scales. The Hospital Anxiety and Depression Scale (HADS) was used to measure patient distress and has demonstrated validity and reliability in a number of populations including individuals with cancer ( Bjelland, Dahl, Haug, & Neckelmann, 2002 ). Patient satisfaction and trust were measured using 0 to 10 numeric rating scales. Numeric observer ratings were used to measure oncologist performance of communication skills based on a videotaped interaction with a standardized patient. Participants completed the same questionnaires at baseline and follow-up.

The authors clearly describe what data were collected from all participants. Providing additional information about the manner in which questionnaires were distributed (i.e., electronic, mail), the setting in which data were collected (e.g., home, clinic), and the design of the survey instruments (e.g., visual appeal, format, content, arrangement of items) would assist the reader in drawing conclusions about the potential for measurement and nonresponse error. The authors describe conducting a follow-up phone call or mail inquiry for nonresponders, using the Dillman et al. ( 2014 ) tailored design for survey research follow-up may have reduced nonresponse error.

CONCLUSIONS

Survey research is a useful and legitimate approach to research that has clear benefits in helping to describe and explore variables and constructs of interest. Survey research, like all research, has the potential for a variety of sources of error, but several strategies exist to reduce the potential for error. Advanced practitioners aware of the potential sources of error and strategies to improve survey research can better determine how and whether the conclusions from a survey research study apply to practice.

The author has no potential conflicts of interest to disclose.

• Help Center
• اَلْعَرَبِيَّةُ
• Deutsch (Schweiz)
• Español (Mexico)
• Bahasa Indonesia
• Bahasa Melayu
• Português (Brasil)
• Tiếng việt

Academic surveys

using expertly-crafted questionnaires and forms to gather reliable data that informs critical decisions for success and growth. Get valuable insights from stakeholders and achieve your objectives while advancing your mission with our powerful academic survey tools.

Unlimited answers

80+ languages, 28+ question types, academic surveys templates.

Student evaluation form template

Course evaluation template

School survey template

Course evaluation form template

Course feedback template

Library registration form template

Student satisfaction survey template

How to create effective academic surveys, tpl_module_accordion_title, 1. define your research question.

Define your research question and objectives before creating the survey questions.

2. MUse clear and concise language

Use clear and concise language, avoiding technical jargon or complicated vocabulary.

3. Make sure the survey questions

Make sure the survey questions are relevant and important to the academic context and the research question.

4. Use different types of questions

Use different types of questions (e.g. multiple choice, Likert scale, open-ended) to gather different types of data.

5. Test the survey with a small sample group

Test the survey with a small sample group before distributing it widely to ensure clarity and effectiveness.

6. Consider the length of the survey

Consider the length of the survey and make sure it is not too long, as this can reduce response rates and increase the likelihood of incomplete responses.

7. Use branching logic to tailor questions

Use branching logic to tailor questions to the respondent's previous answers and ensure that all respondents receive relevant questions.

8. Include demographic questions

Include demographic questions at the end of the survey to gather useful data about the respondents.

9. Use a variety of distribution methods

Use a variety of distribution methods (e.g. email, social media, online forums) to reach a wide audience.

10. Consider offering incentives

Consider offering incentives (e.g. gift cards, free access to research results) to increase response rates and improve the quality of responses.

Academic Survey Builder

Create effective academic surveys easily and efficiently with LimeSurvey's Academic Survey Builder, the user-friendly creator and generator that allows you to gather reliable data and make informed decisions for your academic institution.

• Custom number of responses/year
• Custom upload storage
• Corporate support
• Custom number of alias domains
• Dedicated server
• So much more…

Related templates

Event feedback form template

RSVP form template

Event planning questionnaire template

Conference registration form template

School climate survey template

Similar categories, best academic surveys questionnaires and feedback forms.

Student feedback form template

Parent survey template

Limesurvey is trusted by the world's most curious minds:.

Open Source

Just one more step to your free trial.

.surveysparrow.com

Already using SurveySparrow?  Login

By clicking on "Get Started", I agree to the Privacy Policy and Terms of Service .

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Enterprise Survey Software

Enterprise Survey Software to thrive in your business ecosystem

NPS® Software

Turn customers into promoters

Offline Survey

Real-time data collection, on the move. Go internet-independent.

360 Assessment

Conduct omnidirectional employee assessments. Increase productivity, grow together.

Reputation Management

Turn your existing customers into raving promoters by monitoring online reviews.

Ticket Management

Build loyalty and advocacy by delivering personalized support experiences that matter.

Chatbot for Website

Collect feedback smartly from your website visitors with the engaging Chatbot for website.

Swift, easy, secure. Scalable for your organization.

Executive Dashboard

Customer journey map, craft beautiful surveys, share surveys, gain rich insights, recurring surveys, white label surveys, embedded surveys, conversational forms, mobile-first surveys, audience management, smart surveys, video surveys, secure surveys, api, webhooks, integrations, survey themes, accept payments, custom workflows, all features, customer experience, employee experience, product experience, marketing experience, sales experience, hospitality & travel, market research, saas startup programs, wall of love, success stories, sparrowcast, nps® benchmarks, learning centre, apps & integrations, testimonials, academic research surveys.

Corroborate research findings with real-time data brought by Academic Research Surveys

Please enter a valid Email ID.

14-Day Free Trial • Cancel Anytime • No Credit Card Required • Need a Demo?

Conceptualize & create interactive research surveys to collect data

Share surveys across various platforms and gather research data

Slice & dice the academic research survey data to obtain new perspectives

Consolidate research findings with survey data & present your paper

What are Academic Research Surveys

• Academic Research Survey: Benefits
• Academic Research Surveys: Tips
• Build Academic Research Surveys With SurveySparrow

Whether you are a student, professional, or faculty, the procedure of carrying out studies & evaluating data in order to gain more knowledge about a subject to find answers to different questions. Once completed, the consolidated results are presented in research paper or presentation. Four key characteristics determine the quality of research:

• Ability to enhance knowledge

There are different methodologies for conducting academic research like gathering data from libraries, online databases, internet search engines dedicated to research works and so on. These sources provide secondary information. If you wish to get primary info in hand, the best way is academic research surveys. Once you identify from whom you can gather more knowledge about your subject under study, send out carefully drafted academic research surveys in order to gain valuable insights from it. Thus, you can validate your findings using real-world data.

5 Things You Accomplish With Academic Research Surveys

Academic research opens gates wide for knowledge to flow in. And college-level academic research is an integral part of the curriculum. Here are what you can accomplish by using academic research surveys.

Push Boundaries Beyond Textbook

Academic research helps an individual to go beyond the what their textbooks and references tell them. It exposes them to current models, the evolution of theories, the functioning of applications. These are the things that bring wisdom which a classroom or exams directly cannot provide.

Gain In-depth Knowledge About The Subject

During academic research, an individual is laser-focused on a particular topic of a subject. This helps them gain in-depth wisdom about the topic and anything related to it. Professors often engage in research work to improve the expertise in their fields.

Validate Findings With Solid Data

An academic research survey brings you data from target audience which helps you cross-examine your findings with real-world data and validate your theories.

Help To Stay Up-to-date With Current Tools & Technology

During the process of conducting research, an individual comes across the latest technologies and trending tools. This will develop their skill set and teach them about meticulous execution.

Research Helps Find Opportunities

There’s nothing you cannot find with research. Conducting academic research helps you find new opportunities like scholarships, training grants, project funding, etc.

Get me Started with the Best Academic Research Survey Software

Tips to draft winning academic research surveys.

When you draft your academic research surveys, you must leave no stone unturned to ensure that it is a massive success. So here are five tips to guarantee that is exactly what happens.

Engaging Questionnaires

Ensure that your academic research surveys do not make your respondents dread the idea of taking another survey. For this keep in mind these points. Have a vivid idea about what you wish to gather from your audience. Be precise in what you ask so that your research surveys do not become irritatingly long. Personalize your academic research surveys for this helps to get quality responses and also boost the completion rates.

Diverse Question Types

What you put in your academic research surveys has a catalytic role to play regarding the results and responses you receive. Ensure that your surveys strike the right balance between open-ended and closed-ended questions. Avoid placing leading questions, double-barreled, and vague question types that force respondents to give biased answers, puts them in a tight spot to give accurate answers, or even confuse them.

Conditional Logic Branching

Creating intricate and clever academic research surveys is easy if you deploy the best academic research tool that comes with a multitude of useful features. For example, the logic branching feature skips all the irrelevant questions and poses only what is relevant to your audience. This calculation is done based n the survey taker’s previous answers. When your surveys ask the right questions that pertain to your audience, you get quality answers to questions.

Share Surveys Generously

Now that you know how to create academic research surveys that fetches the best results, it is time to share them with your target audience. Profile your audience according to various demographics and share surveys with them through different channels. These can be email surveys , social media, web links, and so on. Making your academic research surveys multi-device compatible is another great hack to ensure high completion rates.

Draft Stellar Academic Research Surveys With SurveySparrow

Wondering how an academic research platform like SurveySparrow can level-up your research? Here’s how!

Sleek Dual Interface

SurveySparrow offers not just one but two user interfaces to choose from. Engage with your audience using chat-like surveys or conversational forms to get whopping survey completion rates. With survey themes, custom design your themes to create stellar academic research surveys, in no time! The sky is the boundary to what you can do with your online surveys using SurveySparrow survey software!

Share Options

SurveySparrow’s academic research survey platform leaves not a single stone unturned to ensure that the questionnaires you build secure maximum visibility and reach your target audience effectively. SurveySparrow survey software supports many sharing channels such as,

• Email surveys
• Embed option
• Social media

Rich Analytic Dashboard

SurveySparrow not only helps with creating and sharing your academic research survey but analyzing the results as well. The smart dashboard furnishes you with a 360-degree perspective to uncover priceless insights from the data. In this manner, you can recognize various blindspots which were overlooked.

Online Panel

With SurveySparrow’s online panel audience, get access to qualified members belonging to diverse backgrounds. Ask the right people to get the right answers for your academic research surveys.

SurveySparrow offers a plethora of question types to ensure that you get accurate responses from respondents. With a host of open-ended and closed-ended question types, simple ones like star rating to complex ones like matrix type, get maximum data even with short surveys!

Building stellar academic research surveys need considerable effort to be put- if you do not have an outstanding platform by your side. However, with a tool like SurveySparrow, creating, sharing, and analyzing the results of academic research surveys is a simple walk in the park. Browse through our free survey templates that have all popular surveys for market research , employee engagement, customer satisfaction and the likes.

Happy Researching!

I want the Best Academic Research Survey Platform

A quick peek at the reviews.

Head over and read through what users say about us!

See how well we fare over our competitors

Surveygizmo, google forms, our customers love us. want to know why.

"Smart, varied and loaded with opportunity for users to share insights they might not otherwise feel comfortable sharing..."

Katherine Stevens

Market Intelligence & Strategy Manager at Paysafe

Jaakko Jalkanen

Business Manager at Brella

Parisa Vassei

Founder at Parisa Consulting

Peter Vogopoulos

Business Advisor at Exceeding Limits

Try SurveySparrow For Free

Easy to create. Easy to set-up. And much more responses.

Hi there, we use cookies to offer you a better browsing experience and to analyze site traffic. By continuing to use our website, you consent to the use of these cookies. Learn More

Try SurveySparrow for FREE

Claim a 14-day free trial to get a sneak-peek into all the finest features.

20+ SAMPLE Research Questionnaires Templates in PDF | MS Word | Google Docs | Apple Pages

Research questionnaires templates | ms word | google docs | apple pages, 20+ sample research questionnaires templates, what is a research questionnaire, types of research questionnaires, how to make an effective research questionnaire, research questionnaire vs. research survey, the dos and don’ts of a research questionnaire.

Market Research Questionnaire Template

Veterans Health Care Survey Questionnaire

Basic Market Survey Questionnaire

Market Research Questionnaires Format

Research Protocol Survey

Clinical Study PI Questionnaire

Human Study Questionnaire

Research Interest Questionnaire

Research Financial Disclosure Questionnaire

Research Project Questionnaire

Questionnaire for Legal Practitioners

Sample Research Questionnaire

Research Survey Questionnaires

Research Standard Questionnaires

Auto Compensation Research Questionnaire

Orientation Research Questionnaire

Research Questionnaire Format

Social Research Questionnaire

Social Survey Questionnaire

Research into Questionnaire Design

Questionnaires in Clinical Research

1. open-ended questionnaires, 2. closed-ended questionnaires, 3. mixed questionnaires, 4. pictorial questionnaires, step 1: identify the goals of your research questionnaire , step 2: define your target respondents, step 3: create questions , step 4: choose an appropriate question type , step 5: design the sequence and layout of the questions.

• The instrument used for data collection
• Is a tool that is distributed
• May contain open- or closed-ended questions
• Collects information on a topic
• Process of gathering and analyzing data
• Is an activity that is conducted
• Mainly comprised of closed-ended questions
• Aims to draw data for statistical analysis

Dont’s

Share this post on your network, you may also like these articles, research questionnaire.

Research questionnaires are vital tools for gathering data and insights in various fields. This comprehensive guide explores how to design and use them effectively. Whether you are working on…

51+ SAMPLE Food Questionnaire Templates in PDF | MS Word | Google Docs | Apple Pages

A food questionnaire can be used for a lot of purposes by a variety of businesses in the food service, hospitality, catering, and restaurant industry. Developing a food questionnaire is…

browse by categories

• Questionnaire
• Description
• Reconciliation
• Certificate
• Spreadsheet

Information

• privacy policy
• Terms & Conditions

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Surveys Questionnaire

21 Questionnaire Templates: Examples and Samples

Questionnaire: Definition

A questionnaire is defined a market research instrument that consists of questions or prompts to elicit and collect responses from a sample of respondents. A questionnaire is typically a mix of open-ended questions and close-ended questions ; the latter allowing for respondents to enlist their views in detail.

A questionnaire can be used in both, qualitative market research as well as quantitative market research with the use of different types of questions .

LEARN ABOUT: Open-Ended Questions

Types of Questionnaires

We have learnt that a questionnaire could either be structured or free-flow. To explain this better:

• Structured Questionnaires: A structured questionnaires helps collect quantitative data . In this case, the questionnaire is designed in a way that it collects very specific type of information. It can be used to initiate a formal enquiry on collect data to prove or disprove a prior hypothesis.
• Unstructured Questionnaires: An unstructured questionnaire collects qualitative data . The questionnaire in this case has a basic structure and some branching questions but nothing that limits the responses of a respondent. The questions are more open-ended.

LEARN ABOUT:   Structured Question

Types of Questions used in a Questionnaire

A questionnaire can consist of many types of questions . Some of the commonly and widely used question types though, are:

• Open-Ended Questions: One of the commonly used question type in questionnaire is an open-ended question . These questions help collect in-depth data from a respondent as there is a huge scope to respond in detail.
• Dichotomous Questions: The dichotomous question is a “yes/no” close-ended question . This question is generally used in case of the need of basic validation. It is the easiest question type in a questionnaire.
• Multiple-Choice Questions: An easy to administer and respond to, question type in a questionnaire is the multiple-choice question . These questions are close-ended questions with either a single select multiple choice question or a multiple select multiple choice question. Each multiple choice question consists of an incomplete stem (question), right answer or answers, close alternatives, distractors and incorrect answers. Depending on the objective of the research, a mix of the above option types can be used.
• Net Promoter Score (NPS) Question: Another commonly used question type in a questionnaire is the Net Promoter Score (NPS) Question where one single question collects data on the referencability of the research topic in question.
• Scaling Questions: Scaling questions are widely used in a questionnaire as they make responding to the questionnaire, very easy. These questions are based on the principles of the 4 measurement scales – nominal, ordinal, interval and ratio .

Questionnaires help enterprises collect valuable data to help them make well-informed business decisions. There are powerful tools available in the market that allows using multiple question types, ready to use survey format templates, robust analytics, and many more features to conduct comprehensive market research.

LEARN ABOUT: course evaluation survey examples

For example, an enterprise wants to conduct market research to understand what pricing would be best for their new product to capture a higher market share. In such a case, a questionnaire for competitor analysis can be sent to the targeted audience using a powerful market research survey software which can help the enterprise conduct 360 market research that will enable them to make strategic business decisions.

Now that we have learned what a questionnaire is and its use in market research , some examples and samples of widely used questionnaire templates on the QuestionPro platform are as below:

LEARN ABOUT: Speaker evaluation form

Customer Questionnaire Templates: Examples and Samples

QuestionPro specializes in end-to-end Customer Questionnaire Templates that can be used to evaluate a customer journey right from indulging with a brand to the continued use and referenceability of the brand. These templates form excellent samples to form your own questionnaire and begin testing your customer satisfaction and experience based on customer feedback.

LEARN ABOUT: Structured Questionnaire

USE THIS FREE TEMPLATE

Employee & Human Resource (HR) Questionnaire Templates: Examples and Samples

QuestionPro has built a huge repository of employee questionnaires and HR questionnaires that can be readily deployed to collect feedback from the workforce on an organization on multiple parameters like employee satisfaction, benefits evaluation, manager evaluation , exit formalities etc. These templates provide a holistic overview of collecting actionable data from employees.

Community Questionnaire Templates: Examples and Samples

The QuestionPro repository of community questionnaires helps collect varied data on all community aspects. This template library includes popular questionnaires such as community service, demographic questionnaires, psychographic questionnaires, personal questionnaires and much more.

Academic Evaluation Questionnaire Templates: Examples and Samples

Another vastly used section of QuestionPro questionnaire templates are the academic evaluation questionnaires . These questionnaires are crafted to collect in-depth data about academic institutions and the quality of teaching provided, extra-curricular activities etc and also feedback about other educational activities.

MORE LIKE THIS

CX Shenanigans: Booth Duty and Beyond — Tuesday CX Thoughts

Jul 9, 2024

Negative Correlation: Definition, Examples + How to Find It?

Customer Marketing: The Best Kept Secret of Big Brands

Jul 8, 2024

Positive Correlation: What It Is, Importance & How It Works

Jul 5, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

Research Question Generator: Best Tool for Students

Stuck formulating a research question? Try the tool we’ve made! With our research question generator, you’ll get a list of ideas for an academic assignment of any level. All you need to do is add the keywords you’re interested in, push the button, and enjoy the result!

Now, here comes your inspiration 😃

Please try again with some different keywords.

Why Use Research Question Generator?

The choice of research topic is a vital step in the process of any academic task completion. Whether you’re working on a small essay or a large dissertation, your topic will make it fail or fly. The best way to cope with the naming task and proceed to the writing part is to use our free online tool for title generation. Its benefits are indisputable.

• The tool generates research questions, not just topics
• It makes questions focused on your field of interest
• It’s free and quick in use

Research Question Generator: How to Use

Using our research question generator tool, you won’t need to crack your brains over this part of the writing assignment anymore. All you need to do is:

• Insert your study topic of interest in the relevant tab
• Choose a subject and click “Generate topics”
• Grab one of the offered options on the list

The results will be preliminary; you should use them as an initial reference point and refine them further for a workable, correctly formulated research question.

Research Questions: Types & Examples

Depending on your type of study (quantitative vs. qualitative), you might need to formulate different research question types. For instance, a typical quantitative research project would need a quantitative research question, which can be created with the following formula:

Variable(s) + object that possesses that variable + socio-demographic characteristics

You can choose among three quantitative research question types: descriptive, comparative, and relationship-based. Let's consider each type in more detail to clarify the practical side of question formulation.

Descriptive

As its name suggests, a descriptive research question inquires about the number, frequency, or intensity of something and aims to describe a quantitative issue. Some examples include:

• How often do people download personal finance apps in 2022?
• How regularly do Americans go on holidays abroad?
• How many subscriptions for paid learning resources do UK students make a year?

Comparative

Comparative research questions presuppose comparing and contrasting things within a research study. You should pick two or more objects, select a criterion for comparison, and discuss it in detail. Here are good examples:

• What is the difference in calorie intake between Japanese and American preschoolers?
• Does male and female social media use duration per day differ in the USA?
• What are the attitudes of Baby Boomers versus Millennials to freelance work?

Relationship-based

Relationship-based research is a bit more complex, so you'll need extra work to formulate a good research question. Here, you should single out:

• The independent variable
• The dependent variable
• The socio-demographics of your population of interest

Let’s illustrate how it works:

• How does the socio-economic status affect schoolchildren’s dropout rates in the UK?
• What is the relationship between screen time and obesity among American preschoolers?

Research Question Maker FAQ

In a nutshell, a research question is the one you set to answer by performing a specific academic study. Thus, for instance, if your research question is, “How did global warming affect bird migration in California?," you will study bird migration patterns concerning global warming dynamics.

You should think about the population affected by your topic, the specific aspect of your concern, and the timing/historical period you want to study. It’s also necessary to specify the location – a specific country, company, industry sector, the whole world, etc.

A great, effective research question should answer the "who, what, when, where" questions. In other words, you should define the subject of interest, the issue of your concern related to that subject, the timeframe, and the location of your study.

If you don’t know how to write a compelling research question, use our automated tool to complete the task in seconds. You only need to insert your subject of interest, and smart algorithms will do the rest, presenting a set of workable, interesting question suggestions.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• Starting the research process
• Writing Strong Research Questions | Criteria & Examples

Writing Strong Research Questions | Criteria & Examples

Published on October 26, 2022 by Shona McCombes . Revised on November 21, 2023.

A research question pinpoints exactly what you want to find out in your work. A good research question is essential to guide your research paper , dissertation , or thesis .

All research questions should be:

• Focused on a single problem or issue
• Researchable using primary and/or secondary sources
• Feasible to answer within the timeframe and practical constraints
• Specific enough to answer thoroughly
• Complex enough to develop the answer over the space of a paper or thesis
• Relevant to your field of study and/or society more broadly

Table of contents

How to write a research question, what makes a strong research question, using sub-questions to strengthen your main research question, research questions quiz, other interesting articles, frequently asked questions about research questions.

You can follow these steps to develop a strong research question:

• Choose your topic
• Do some preliminary reading about the current state of the field
• Narrow your focus to a specific niche
• Identify the research problem that you will address

The way you frame your question depends on what your research aims to achieve. The table below shows some examples of how you might formulate questions for different purposes.

Using your research problem to develop your research question

Note that while most research questions can be answered with various types of research , the way you frame your question should help determine your choices.

Prevent plagiarism. Run a free check.

Research questions anchor your whole project, so it’s important to spend some time refining them. The criteria below can help you evaluate the strength of your research question.

Focused and researchable

Feasible and specific

Complex and arguable

Relevant and original

Chances are that your main research question likely can’t be answered all at once. That’s why sub-questions are important: they allow you to answer your main question in a step-by-step manner.

Good sub-questions should be:

• Less complex than the main question
• Focused only on 1 type of research
• Presented in a logical order

Here are a few examples of descriptive and framing questions:

• Descriptive: According to current government arguments, how should a European bank tax be implemented?
• Descriptive: Which countries have a bank tax/levy on financial transactions?
• Framing: How should a bank tax/levy on financial transactions look at a European level?

Keep in mind that sub-questions are by no means mandatory. They should only be asked if you need the findings to answer your main question. If your main question is simple enough to stand on its own, it’s okay to skip the sub-question part. As a rule of thumb, the more complex your subject, the more sub-questions you’ll need.

Try to limit yourself to 4 or 5 sub-questions, maximum. If you feel you need more than this, it may be indication that your main research question is not sufficiently specific. In this case, it’s is better to revisit your problem statement and try to tighten your main question up.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

As you cannot possibly read every source related to your topic, it’s important to evaluate sources to assess their relevance. Use preliminary evaluation to determine whether a source is worth examining in more depth.

This involves:

• Reading abstracts , prefaces, introductions , and conclusions
• Looking at the table of contents to determine the scope of the work
• Consulting the index for key terms or the names of important scholars

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

Formulating a main research question can be a difficult task. Overall, your question should contribute to solving the problem that you have defined in your problem statement .

However, it should also fulfill criteria in three main areas:

• Researchability
• Feasibility and specificity
• Relevance and originality

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 21). Writing Strong Research Questions | Criteria & Examples. Scribbr. Retrieved July 10, 2024, from https://www.scribbr.com/research-process/research-questions/

Is this article helpful?

Shona McCombes

Other students also liked, how to define a research problem | ideas & examples, how to write a problem statement | guide & examples, 10 research question examples to guide your research project, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

academic performance research questionnaire sample

Related Papers

Nova Degorio , Katsikas Elias

Indonesian Journal of Educational Research and Technology

Rezel Dagamac

Academic achievement is one of the essential elements that businesses examine when employing new graduates. This problem calls for intensive survey investigation to find out the Factors Affecting the Academic Performance of Grade 11 students at SKSU-Laboratory High School. This research was conducted to identify the factors affecting the academic performance of students at SKSU-Laboratory High School, which would help provide suggestions that would promote better academic performance that would interest the students in the study area. This study used a questionnaire for quantitative research instruments and has also used a customized method such as an online survey for the study. This study was conducted to explore the different factors that affect students’ academic performance. This study states that the students' environment has excellent effects, enhancing or weakening their academic performance. There are various factors inside and outside school that contribute to the qual...

Chika Albert

Sheena Mae T . Comighud, EdD , Maricel Malbog

This study examined the academic performance attributes of the selected grade six pupils of the identified public elementary schools of SDO-Bayawan City. It employed descriptive method. A questionnaire was used to generate data that measuredthe variables. It was administered to the respondents and the answers generated were tabulated and analyzed. Based on the findings of the study, it can be concluded that the academic performance of the grade six pupils was at satisfactory level. It is recommended that the proposed programs, activities, and projects can be adopted by the public elementary schools of Bayawan City Division.

International Journal of Statistics and Applied Mathematics

Katleho Makatjane

In this paper, the PLS-PM model has been estimated as to directly and indirectly identify factors that influence academic performance of the first year students at NUL. Sample used to utilise the task was 46. The estimated PLS-PM model was found stable and satisfying the SEM conditions. Several measure were established and found that 63% of variation of OWM is been explained by all those factors that are found to be significant. Also, seven factors were retained with factor loadings in the range of 0.4 to 0.81. Furthermore, the results of the discriminant analysis revealed that, 54% of female students are enrolled to the university while only 46% is for male students each year. 1. Introduction In all countries of the world, education is the most important sector of living; hence the major resources are plunged into it as an investment to human resource and the development of the country. The educational performance is influenced by various components including admission points, socio economic status and school foundation. Acato (2006) [1] ; Geiser and Santelices (2007) [18] all contend that admission points which are a reflection of the past performance has some impact on future performance of students. Tertiary institutions in Austria have found that a selection rank based on a student's overall performance is a predictor of success for most courses. As documented by Berthelot, Ross, and Tremblay (2001) [5] , the study agrees with the literature that admission points really distress the performance of university students and that is why the basic university entry admission points is a diploma points or mature age points. However, Berg (2012) [4] defines education as the conveyance of learning, aptitudes and information from teachers to students is lacking to capture what is truly vital about being and getting to be educated. Learning is taken to mean any change in behavior, knowledge, understanding, skills or capabilities which the greenhorn retains which cannot be ascribed simply to the physical growth or to the development of inherited behavior patterns. In the current study, two techniques are used to check two different issues. The first technique is the use of the structural equation model (SEM) through employment of the Partial Least Square Path Model (PLS-PM) to identify the factors that influences the academic performance of first year students at the National University of Lesotho (NUL) directly and indirectly. And lastly, in assessing the enrollment rate at the university, the K th nearest neighbor discriminant analysis with the discriminating factor as the sex structure of the student is engaged.

British Journal of Educational Psychology

tim brennan

Bowo Mantap

AubRey Bermudez

Justine Martin

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Oluwafemi Samson Balogun

Educational Psychology

Gregory Arief Liem

Bereket Gebre

Muhammad Daniyal

Jordi Longás Mayayo

Adaeze Chike-Okoli

Cyreanth Trujulan

International Journal of Vocational Education and Training Research

Solomon Tibebu

Andrew Riechi

British Journal of Education Society Behavioural Science

mohammed sulemana

International Journal of Social and Educational Innovation

Liliana BUJOR

International Journal of Social Service and Research

Dr. Dimas Agung Trisliatanto, S.IIP., M.PSDM., CHCM., CHRGA., CHRS., CHRRSS.

Assem Humphrey Darkeh

JOSEPHAT NCHUNGO

KENNETH ABBAN

Kenneth Abban

IEA Research for Education

rezana vrapi

Wendell Glenn Sudaria

mary grace ann vinluan

Musa Balarabe

Sylvester Onyeka

KnE Social Sciences

Zalinawati Abdullah

Journal of Education and Practice

Habtamu Legese

tamseel asim

Harlette Laguidao

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Frequently Asked Questions

Why am i never asked to take a poll.

You have roughly the same chance of being polled as anyone else living in the United States. This chance, however, is only about 1 in 170,000 for a typical Pew Research Center survey. To obtain that rough estimate, we divide the current adult population of the U.S. (about 255 million) by the typical number of adults we recruit to our survey panel each year (usually around 1,500 people). We draw a random sample of addresses from the U.S. Postal Service’s master residential address file. We recruit one randomly selected adult from each of those households to join our survey panel. This process gives every non-institutionalized adult a known chance of being included. The only people who are not included are those who do not live at a residential address (e.g., adults who are incarcerated, living at a group facility like a rehabilitation center, or living in a remote area without a standard postal address).

Can I volunteer to be polled?

While we appreciate people who want to participate, we can’t base our polls on volunteers. The key to survey research is to have a  random sample so that every person has a chance of having their views captured. The kinds of people who might volunteer for our polls are likely to be very different from the average American – at the very least they would probably be more politically interested and engaged, which would not be a true representation of the general population.

Why should I participate in surveys?

Polls are a way for you to express your opinions to the nation’s leaders and the country as a whole. Public officials and other leaders pay attention to the results of polls and often take them into account in their decision-making. If certain kinds of people do not participate in the surveys, then the results won’t represent the full range of opinions in the nation.

What good are polls?

Polls seek to measure public opinion and document the experiences of the public on a range of subjects. The results provide information for academics, researchers and government officials and help to inform the decision-making process for policymakers and others. Much of what the country knows about its media usage, labor and job markets, educational performance, crime victimization and social conditions is based on data collected through polls.

Do pollsters have a code of ethics? If so, what is in the code?

The major professional organizations of survey researchers have very clear codes of ethics for their members. These codes cover the responsibilities of pollsters with respect to the treatment of respondents, their relationships with clients and their responsibilities to the public when reporting on polls.  Some good examples of a pollster’s Code of Ethics include:

American Association for Public Opinion Research (AAPOR)

Council of American Survey Research Organizations (CASRO)

You can read Pew Research Center’s mission and code of ethics  here .

How are your polls different from market research?

One main difference is the subject matter. Market research explores opinions about products and services and measures your buying patterns, awareness of products and services or willingness to buy something. Our polls typically focus on public policy issues, mainly aimed at informing the public. We also try to measure topics like how voters are reacting to candidates in political campaigns and what issues are important to them.

Do you survey Asian Americans?

Yes. Our surveys are representative of the entire adult population of the United States and accurately account for the full population’s diversity by age, gender, race and ethnicity, region, and socioeconomic factors such as education levels, household income and employment status. We do not exclude anyone from our analyses based on his or her demographic characteristics. With the American Trends Panel, the Center release results specifically for Asian Americans in multiple reports each year.

How are people selected for your polls?

Most of our U.S. surveys are conducted on the American Trends Panel (ATP), the Center’s national survey panel of over 10,000 randomly selected U.S. adults. ATP participants are recruited offline using random sampling from the U.S. Postal Service’s residential address file. Respondents complete the surveys online using smartphones, tablets or desktop devices. We provide tablets and data plans to adults without home internet.

Do people lie to pollsters?

We know that not all survey questions are answered accurately, but it’s impossible to gauge intent and to say that any given inaccurate answer necessarily involves lying. People may simply not remember their behavior accurately.

More people say they voted in a given election than voting records indicate actually cast ballots. In some instances, researchers have actually verified the voting records of people who were interviewed and found that some of them said they voted but did not. Voting is generally considered a socially desirable behavior, like attending church or donating money to charity. Studies suggest these kinds of behaviors are overreported. Similarly, socially undesirable behaviors such as illegal drug use, certain kinds of sexual behavior or driving while intoxicated are underreported.

We take steps to minimize errors related to questions about socially desirable or undesirable activities. For example, questions about voter registration and voting usually acknowledge that not everyone takes part in elections. Pew Research Center’s voter turnout question is worded this way:

“Which of the following statements best describes you? I did not vote in the [YEAR] presidential election; I planned to vote but wasn’t able to; I definitely voted in the [YEAR] presidential election”

Do people really have opinions on all of those questions?

When we poll on a topic that may be unfamiliar, we typically start by asking how much, if anything, people have heard about it. This way we can get some insight into who knows about the subject and who does not. When we release results from the poll, we typically report just the opinions of people who say they had heard about the topic, and we also report what share of the public had not heard about the topic.

How can I tell a high-quality poll from a lower-quality one?

Two key aspects to consider are transparency and representation. Pollsters who provide clear, detailed explanations about how the poll was conducted (and by whom) tend to be more accurate than those who do not. For example, reputable pollsters will report the source from which the sample was selected, the mode(s) used for interviewing, question wording, etc. High-quality polls also have procedures to ensure that the poll represents the public, even though response rates are low, and some groups are more likely to participate in polls than others. For example, it helps to sample from a database that includes virtually all Americans (e.g., a master list of addresses or phone numbers). Also, it is critical that the poll uses a statistical adjustment (called “weighting”) to make sure that it aligns with an accurate profile of the public. For example, Pew Research Center polls adjust on variables ranging from age, sex and education to voter registration status and political party affiliation. More general guidelines on high-quality polling are available here .

How can a small sample of 1,000 (or even 10,000) accurately represent the views of 250,000,000+ Americans?

Two main statistical techniques are used to ensure that our surveys are representative of the populations they’re drawn from: random sampling and weighting. Random sampling ensures that each person has the same chance of selection to participate in a survey and that the people selected into a sample are a good mix of various demographics, such as age, race, income and education, just like in the general population. However, sample compositions can differ. For example, one sample drawn from a nationally representative list of residential addresses may have a higher percentage of rural dwellers compared with another sample drawn from the exact same list. To ensure that samples drawn ultimately resemble the population they are meant to represent, we use weighting techniques in addition to random sampling. These weighting techniques adjust for differences between respondents’ demographics in the sample and what we know them to be at population level, based on information obtained through institutions such as the U.S. Census Bureau. For more on this topic, check out our Methods 101 video on random sampling.

Do your surveys include people who are offline?

Yes. For the online ATP panel to be truly nationally representative, the share of those who do not use the internet nationally must be represented on the panel. In the past, we did this by providing identified non-internet users with paper questionnaires to complete and mail back. Now, those who don’t have internet access are provided with internet-enabled tablets to take their surveys. These tablet-provided individuals are representative of our non-internet population in the Center’s analyses.  

U.S. Surveys

Other research methods.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

© 2024 Pew Research Center

College & Research Libraries ( C&RL ) is the official, bi-monthly, online-only scholarly research journal of the Association of College & Research Libraries, a division of the American Library Association.

C&RL is now on Instragram! Follow us today.

Leo S. Lo is Dean, College of University Libraries and Learning Sciences at the University of New Mexico, email: [email protected] .

C&RL News

ALA JobLIST

Advertising Information

• Research is an Activity and a Subject of Study: A Proposed Metaconcept and Its Practical Application (76908 views)
• Information Code-Switching: A Study of Language Preferences in Academic Libraries (40088 views)
• Three Perspectives on Information Literacy in Academia: Talking to Librarians, Faculty, and Students (28581 views)

Evaluating AI Literacy in Academic Libraries: A Survey Study with a Focus on U.S. Employees

Leo S. Lo *

This survey investigates artificial intelligence (AI) literacy among academic library employees, predominantly in the United States, with a total of 760 respondents. The findings reveal a modest self-rated understanding of AI concepts, limited hands-on experience with AI tools, and notable gaps in discussing ethical implications and collaborating on AI projects. Despite recognizing the benefits, readiness for implementation appears low among participants. Respondents emphasize the need for comprehensive training and the establishment of ethical guidelines. The study proposes a framework defining core components of AI literacy tailored for libraries. The results offer insights to guide professional development and policy formulation as libraries increasingly integrate AI into their services and operations.

Introduction

In a world increasingly dictated by algorithms, artificial intelligence (AI) is not merely a technological phenomenon, it is a transformative force that redefines our intellectual, social, and professional landscapes (McKinsey and Company, 2023). The rapid integration of AI in our everyday lives has profound implications for higher education, a sector entrusted with preparing individuals to navigate, contribute to, and thrive in this AI-driven era. From personalized learning environments to automated administrative tasks, AI’s influence in higher education is omnipresent and its potential boundless. However, this potential can only be harnessed effectively if those at the frontline of academia—our educators, researchers, administrators, and, notably, academic library employees—are equipped with the necessary AI literacy (UNESCO, 2021). Without an understanding of AI’s principles, capabilities, and ethical considerations, higher education risks falling prey to AI’s pitfalls rather than leveraging its benefits.

The potential risks and benefits underscore a pressing need to scrutinize and elevate AI literacy within the higher education community—a task that begins with understanding its current state. As facilitators of information and knowledge, academic library employees stand at the crossroads of this AI revolution, making their AI literacy an imperative, not a choice, for the future of higher education.

AI Literacy: Context and Background

In an era marked by exponential growth in digital technology, the concept of literacy has evolved beyond traditional reading and writing skills to encompass a wide array of digital competencies. One such competency, which is gaining critical importance in higher education, is AI literacy. With AI systems beginning to permeate every facet of university operations—from learning management systems to research analytics—the ability to understand and navigate these AI tools has become an essential skill for academic library employees.

AI literacy, a subset of digital literacy, specifically pertains to understanding AI’s principles, applications, and ethical considerations. It involves not only the ability to use AI tools effectively, but also the capacity to evaluate their outputs critically, to understand their underlying mechanisms, and to contemplate their ethical and societal implications. AI literacy is not just for computer professionals; as Lo (2023b) and Cetindamar et al. (2022) emphasize, operationalizing AI literacy for non-specialists is essential.

The significance of AI literacy in higher education is underscored by several contemporary trends and challenges. Companies and governments globally are engaged in fierce competition to stay at the forefront of AI integration. Concurrently, the rapid proliferation of AI is giving rise to a host of ethical and privacy concerns that require informed stewardship (Cox, 2022). Furthermore, the COVID-19 pandemic has accelerated the digital transformation of higher education, leading to an increased reliance on AI technologies for remote learning and operations. This reliance further points to the necessity of AI literacy among academic library employees, who play a pivotal role in facilitating online learning and research.

As artificial intelligence proliferates across higher education, developing AI literacy is increasingly recognized as a priority to prepare students, faculty, staff, and administrators to harness AI’s potential, while mitigating risks (Ng et al., 2021). Hervieux and Wheatley’s (2021) 2019 study (n=163) found that academic librarians require more training regarding artificial intelligence and its potential applications in libraries. The U.S. Department of Education’s recent report (2023) on AI emphasizes the growing importance of AI literacy for educators and students, highlighting the necessity of understanding and integrating AI technologies in educational settings. This report aligns with the broader discourse on AI literacy and emphasizes the need to equip library professionals with skills needed to evaluate and utilize AI tools effectively (Lo, 2023a).

While efforts to promote AI literacy are growing, the required content for different target groups remains ambigu­ous. Some promising measurement tools have been proposed, such as Pinski and Benlian’s (2023) multidimensional scale assessing perceived knowledge of AI technology, processes, collaboration, and design. However, further validation of AI literacy assessments is required. Developing rigorous definitions and measurements is crucial for implementing effective AI literacy initiatives.

Ridley and Pawlick-Potts (2021) put forth the concept of algorithmic literacy, involving understanding algorithms and their influence, recognizing their uses, assessing their impacts, and positioning individuals as active agents rather than passive recipients of algorithmic decision-making. They propose libraries can contribute to algorithmic literacy by integrating it into information literacy education and supporting explainable AI.

Ocaña-Fernández et al. (2019) argued curriculum and skills training changes are critical to prepare students and faculty for an AI future, though also warn about digital inequality issues. Laupichler et al.’s (2022) scoping review reveals efforts to teach foundational AI literacy to non-specialists are still in formative stages. Proposed essential skills vary considerably across frameworks, and robust evaluations of AI literacy programs are lacking. Findings indicate that carefully designed AI literacy courses show promise for knowledge gains; however, research substantiating appropriate frameworks, core competencies and effective instructional approaches for diverse audiences remains an open need.

Within libraries, Heck et al. (2019) discussed the interplay of information literacy and AI. They propose that AI could aid information literacy teaching through timely feedback and tracking skill development, but note that common evaluation approaches would need establishing first. Information literacy empowers learners to actively engage with, not just passively consume from, AI systems. Lo (2023c) proposed a framework to utilize prompt engineering to enhance information literacy and critical thinking skills.

Oliphant (2015) examined intelligent agents for library reference services. The analysis found they rapidly retrieve information but lack human evaluation abilities. Findings suggest librarians will need to guide users in critically evaluating AI-generated results, indicating that information literacy instruction remains crucial. Furthermore, Lund et al. (2023) discuss the ethical implications of using large language models, such as ChatGPT, in scholarly publishing, emphasizing the need for ethical considerations and the potential impact of AI on research practices.

While research is still emerging, initial findings highlight the need for rigorous, tailored AI literacy initiatives encompassing technical skills, critical perspectives, and ethical considerations. As AI becomes further entwined with education and work, developing validated frameworks, assessments, and instructional approaches to enhance multidimensional AI literacy across contexts and roles is an urgent priority. This study seeks to contribute by investigating AI literacy specifically among academic library employees.

Purpose of the Study

The rapid pace of AI development and integration in higher education heightens the need to address this research gap. As AI continues to evolve and permeate further into academic libraries, the demand for AI-literate library employees will only increase. Failure to understand the current state of AI literacy, and to identify the gaps, could result in a significant skills deficit that would impedes the effective utilization of AI in academic libraries.

In light of this, the purpose of this study is to embark on an investigation of AI literacy among academic library employees. The study seeks to answer the following critical research questions:

• What is the current level of AI literacy among academic library employees?
• What gaps exist in their AI literacy, and how can these gaps be addressed through professional development and training programs?
• What are their perceptions of generative AI, and what implications do they foresee for the library profession?

By addressing these questions, this study aims to fill a research gap and provide insights that can inform policy and practice in higher education. It strives to shed light on the competencies that academic library employees possess, identify the gaps that need to be addressed, and propose strategies for enhancing AI literacy among this essential group of higher education professionals.

Theoretic Framework

The Technological Pedagogical Content Knowledge (TPACK) framework developed by Mishra and Koehler (2006) serves as the theoretical foundation for this study. TPACK has also been advocated as a useful decision-making structure for librarians evaluating instructional technologies (Sobel & Grotti, 2013).

Mishra and Koehler (2006) explain that TPACK involves flexible, context-specific application of technology, pedagogy, and content knowledge. It goes beyond isolated knowledge of the concepts to an integrated understanding. TPACK development requires moving past viewing technology as an “add-on” and focusing on the connections between technology, content, and pedagogy in particular educational contexts.

In the context of this study, the researcher applied the TPACK framework to examine AI literacy specifically among academic library professionals. The three key components of the TPACK framework are interpreted as:

• Technological Knowledge (TK)—Knowledge about AI itself, including its principles, capabilities, and limitations. This encompasses understanding AI as a technology and its potential applications in library settings.
• Pedagogical Knowledge (PK)—Knowledge about how AI can be used to enhance library services and facilitate learning. This relates to understanding how AI can be integrated into library services to improve user experience, streamline operations, and support learning.
• Content Knowledge (CK)—Knowledge about the library’s content and services. This involves perceiving the potential impact of AI on the library’s content and services, and how AI can enhance their management and delivery.

This tailored application of the TPACK framework will allow a multidimensional assessment of AI literacy among academic library employees. It facilitates examining employees’ understanding of AI as a technology (TK), perceptions of how AI can enhance library services (PK), and the potential impact of AI on the library’s content and services (CK).

Significance of the Study

The significance of this study lies in its potential to contribute to academic library policy, practice, and theory in several ways. Firstly, it utilizes the TPACK framework to evaluate AI literacy among academic library employees, identifying competencies, gaps, and necessary strategies. This insight is crucial for designing effective professional development programs, as well as for resource allocation. Secondly, it adds to the discourse on digital literacy in higher education by specifically focusing on AI literacy, aiding in understanding its role and implications. Thirdly, the study provides insights into the ethical, practical, and opportunity dimensions of AI technology integration in libraries, informing best practices and guidelines for its responsible use. Lastly, by applying the TPACK framework to AI literacy in libraries, the study expands its theoretical applications and offers a robust basis for future research in technology integration in academic settings.

Methodology

Research design.

This study employs a survey-based approach to explore AI literacy among academic library employees, chosen for its ability to quickly gather extensive data across a geographically diverse group. The method aligns with the TPACK framework, highlighting the integration of technological, pedagogical, and content knowledge. Surveys facilitate the collection of standardized data, allowing for comparisons across different roles and demographics. This design is particularly effective for descriptive research in higher education, making it suitable for assessing the current state of AI literacy in academic libraries.

Participants

The researcher utilized a comprehensive approach to recruit a diverse group of academic library employees for the survey. This involved posting on professional listservs across various roles and regions in librarianship (Appendix A), as well directly contacting directors of prominent library associations: the Association of Research Libraries (ARL), the Greater Western Library Alliance (GWLA), and the New Mexico Consortium of Academic Libraries (NMCAL). These organizations represent a broad spectrum of academic libraries in terms of size, location, and type. The directors were requested to share the survey with their staff, thus ensuring a wide-reaching and representative sample for the study.

Data Collection

Data collection was facilitated through a custom-designed survey instrument, which was built and administered using the Qualtrics platform (Appendix B). The survey itself was developed to address the study’s research questions and was structured into four main sections, each focusing on a specific aspect of AI literacy among academic library employees.

The first section sought to capture respondents’ understanding and knowledge of AI, including their familiarity with AI concepts and terminology. The second section focused on respondents’ practical skills and experiences with AI tools and applications in professional settings. The third section aimed to identify areas of AI literacy where respondents felt less confident, signaling potential gaps in knowledge or skills that could be addressed through professional development initiatives. Finally, the last section explored respondents’ perspectives on the ethical implications and challenges presented by AI technologies in the library context.

The survey employed a mix of question types to engage respondents and capture nuanced data. These included Likert-scale questions, multiple choice, and open-ended questions. Prior to the full-scale administration, the survey was pilot-tested with a small group of academic library employees to ensure clarity, relevance, and appropriateness of the questions.

The survey questions were designed to tap into different dimensions of the TPACK framework. For instance, questions asking about practical experiences with AI tools and self-identified areas of improvement indirectly assess the intersection of technological and pedagogical knowledge (TPK), as they relate to AI.

Upon finalizing the survey, an invitation to participate, along with a link to the survey, was distributed via the listservs and direct outreach methods. The survey remained open for two weeks, with reminders sent out at regular intervals to maximize the response rate.

Limitations

While the study offers insights into AI literacy among academic library employees, it is crucial to acknowledge its limitations. Firstly, given the survey’s self-report nature, the findings may be subject to social desirability bias, where respondents might have over- or under-estimated their knowledge or skills in AI.

Secondly, despite best efforts to reach a wide range of academic library employees, the sample may not be entirely representative of the population. The voluntary nature of participation, coupled with the distribution methods used, may have skewed the sample towards those with an existing interest or engagement in AI.

Moreover, while the use of professional listservs and direct outreach to library directors helped widen our reach, this strategy might have excluded those academic library employees who are less active, or not included, in these communication channels. The inclusion of Canadian libraries through the Association of Research Libraries suggests a small number of non-U.S. respondents.

Finally, the rapidly evolving nature of AI and its applications in libraries means that our findings provide a snapshot at a specific point in time. As AI continues to advance and integrate more deeply into academic libraries, the landscape of AI literacy among library employees is likely to shift, necessitating ongoing research in this area.

These limitations, while important to note, do not invalidate our findings. Instead, they offer points of consideration for interpreting the results and highlight areas for future research to build on our understanding of AI literacy among academic library employees.

Results and Analysis

Descriptive statistics.

The survey drew a diverse response: 760 participants started the survey, 605 completed it. The participants represented a cross-section of the academic library landscape, with the majority (45.20%) serving in Research Universities. A significant proportion also hailed from institutions offering both graduate and undergraduate programs (29.64%) and undergraduate-focused Colleges or Universities (10.76%). Community Colleges and specialized professional schools (e.g., Law, Medical) were represented as well, albeit to a lesser extent.

Over half of the respondents (61.25%) were from libraries affiliated with the Association of Research Libraries (ARL), signifying an extensive representation from research-intensive institutions. Respondents were predominantly from larger academic institutions. Those serving in institutions with enrollments of 30,000 or more made up the largest group (30.67%), closely followed by those in institutions with enrollments ranging from 10,000 to 29,999 (34.66%).

As for professional roles, the survey drew heavily from the library specialists or professionals (60.99%) who directly support the academic community’s research, learning, and teaching needs. Middle (20.00%) and senior (9.09%) management personnel were also well-represented, providing a leadership perspective to the survey insights.

Most of the respondents were primarily involved in Reference and Research Services (25.17%) or Library Instruction and Information Literacy (24.34%)—two areas integral to the academic support infrastructure.

In terms of professional experience, participants exhibited a broad range, from novices with less than a year’s experience (2.81%) to seasoned veterans with over 20 years in the field (22.68%).

The survey group was highly educated, with most holding a master’s degree in library and information science (65.51%), and a significant number having completed a doctoral degree or a master’s in another field.

The survey also collected demographic information. A substantial majority identified as female (71.97%), and the largest age group was 35–44 years (27.97%). While the majority identified as White (76.11%), other ethnicities, including Asian, Black or African American, and Hispanic or Latino, were also represented.

This diverse participant profile offers a broad-based view of AI literacy in the academic library landscape, setting the stage for insightful findings and discussions.

RQ 1 AI Literacy Levels

At a broad level, participants expressed a modest understanding of AI concepts and principles, with a significant portion rating their knowledge at an average level. However, the number of respondents professing a high understanding of AI was quite small, revealing a potential area for further training and education.

A similar pattern was observed when participants were queried about their understanding of generative AI specifically. This suggests that while librarians have begun to grasp AI and its potential, there is a considerable scope for growth in terms of knowledge and implementation (Figure 1).

Regarding the familiarity with AI tools, most participants had a moderate level of experience (30.94%). Only a handful of participants reported a high level of familiarity (3.87%), signaling an opportunity for more hands-on training with these tools.

In examining the prevalence of AI usage in the library sector, the researcher found a varied landscape. While some technologies have found significant adoption, others remain relatively unused. Notably, Chatbots and text or data mining tools were the most widely used AI technologies.

Participants’ understanding of specific AI concepts followed a similar trend. More straightforward concepts such as Machine Learning and Natural Language Processing had a higher average rating, whereas complex areas like Deep Learning and Generative Adversarial Networks were less understood. This trend underscores the need for targeted educational programs on AI in library settings.

Notably, there was almost a nine percent drop in responses from the previous questions to the questions that asked about the more technical aspects of AI. This could signify a gap in knowledge or comfort level with these topics among the participants.

In the professional sphere, AI tools have yet to become a staple in library work. The majority of participants do not frequently use these tools, with 41.79% never using generative AI tools and 28.01% using them less than once a month. This might be attributed to a lack of familiarity, resources, or perceived need. However, for those who do use them, text generation and research assistance are the primary use cases.

Concerns about ethical issues, quality, and accuracy of generated content, as well as data privacy, were prevalent among the participants. This finding indicates that while there’s interest in AI technologies, the perceived challenges are significant barriers to full implementation and adoption.

In their personal lives, AI tools have yet to make a significant impact among the participants. The majority (63.98%) reported using these tools either ‘less than once a month’ or ‘never.’ This could potentially reflect the current state of AI integration in non-professional or leisurely activities, and may change as AI continues to permeate our everyday lives.

A chi-square test of independence was performed to examine the relation between the position of the respondent and the understanding of AI concepts and principles. The relation between these variables was significant, χ 2 (16, N = 760) = 26.31, p = .05. This means that the understanding of AI concepts and principles varies depending on the position of the respondent.

The distributions suggest that—while there is a significant association between the position of the respondent and their understanding of AI concepts and principles—the majority of respondents across all positions have a moderate understanding of AI. However, there are differences in the proportions of respondents who rate their understanding as high or very high, with Senior Management and Middle Management having higher proportions than the other groups.

There is also a significant relation between the area of academic librarianship and the understanding of AI concepts and principles, χ²(36, N = 760) = 68.64, p = .00084. This means that the understanding of AI concepts and principles varies depending on the area of academic librarianship. The distributions show that there are differences in the proportions of respondents who rate their understanding as high or very high, with Administration or management and Library Instruction and Information Literacy having higher proportions than the other groups.

Furthermore, a Chi-Square test shows that the relation between the payment for a premium version of at least one of the AI tools and the understanding of AI concepts and principles is significant, χ²(4, N = 539) = 85.42, p < .001. The distributions suggest that respondents who have paid for a premium version of at least one of the AI tools have a higher understanding of AI concepts and principles compared to those who have not. This could be because those who have paid for a premium version of an AI tool are more likely to use AI in their work or personal life, which could enhance their understanding of AI. Alternatively, those with a higher understanding of AI might be more likely to see the value in paying for a premium version of an AI tool.

It’s important to note that these findings are based on the respondents’ self-rated understanding of AI, which may not accurately reflect their actual understanding. Further research could involve assessing the respondents’ understanding of AI through objective measures. Additionally, other factors not considered in this analysis, such as the respondent’s educational background, years of experience, and exposure to AI in their work, could also influence their understanding of AI.

RQ2 Identifying Gaps

In this section, the researcher delved deeper into the gaps in knowledge and confidence among academic library professionals regarding AI applications. These gaps highlight the urgent need for targeted professional development and training in AI literacy.

Confidence Levels in Various Aspects of AI

The survey data pointed to moderate levels of confidence across a spectrum of AI-related tasks, indicating room for growth and learning. For evaluating ethical implications of using AI, a modest 30.12% of respondents felt somewhat confident (levels 4 and 5 combined), while 29.50% were not confident (levels 1 and 2 combined), and the largest group (39.38%) remained neutral.

Discussing AI integration revealed similar patterns. Here, 31.1% reported high confidence, 34.85% expressed low confidence, and the remaining 33.06% were neutral. These distributions suggest an overall hesitation or lack of assurance in discussing and ethically implementing AI, potentially indicative of inadequate training or exposure to these topics.

When it came to collaborating on AI-related projects, fewer respondents (31.39%) felt confident, while 40.16% reported low confidence, and 28.46% chose a neutral stance. This might point to the necessity of not only individual proficiency in AI but also the need for collaborative skills and shared understanding among teams working with AI.

Troubleshooting AI tools and applications emerged as the most significant gap, with 69.76% rating their confidence as low and only 10.9% expressing high confidence. This highlights an essential area for targeted training, as troubleshooting is a fundamental aspect of successful technology implementation.

Reflecting on Professional Development and Training in AI

Approximately one-third of survey participants have engaged in AI-focused professional development, showcasing several key themes:

• Modes of Training: Librarians access training via various formats, including webinars, workshops, and self-guided learning. Online options are popular, providing accessibility for diverse professionals.
• AI Tools and Applications: Training sessions mainly introduce tools like ChatGPT and others, with an emphasis on functionality and applications in academia.
• Ethical Implications: Sessions often address ethical concerns such as bias and privacy, and the potential misuse of ‘black box’ AI models.
• Integration into Librarian Workflows: Programs explore AI’s integration into library work, including instruction, cataloging, and citation analysis.
• AI Literacy: There is a recurring focus on understanding and teaching AI concepts, tied to broader information literacy discussions.
• AI in Instruction: Training includes using AI tools in library instruction and understanding its impacts on academic integrity.
• Community of Practice: Responses highlight collaborative learning, suggesting a communal approach to understanding AI’s challenges and opportunities.
• Self-guided Learning: Some librarians actively pursue independent learning opportunities, reflecting a proactive stance on AI professional development.

The findings emphasize the multifaceted nature of AI in libraries, underlining the need for ongoing, comprehensive professional development. This includes addressing both technical and ethical aspects, equipping librarians with practical AI skills, and fostering a supportive community of practice.

A Chi-square test examining the relationship between the respondents’ positions and their participation in any training focused on generative AI (χ²(4, N = 595) = 26.72, p < .001) indicates a significant association. Upon examining the data, the proportion of respondents who have participated in training or professional development programs focused on generative AI is highest among those in Senior Management (47.27%), followed by Specialist or Professional (37.40%), Middle Management (29.75%), and Other (16.67%). The proportion is lowest among Support Staff or Administrative (3.70%).

This suggests that individuals in higher positions, such as Senior Management and Specialist or Professional roles, are more likely to have participated in training or professional development programs focused on generative AI. This could be due to a variety of reasons, such as these roles potentially requiring a more in-depth understanding of AI and its applications, or these individuals having more access to resources and opportunities for such training. On the other hand, Support Staff or Administrative personnel are less likely to have participated in such programs, which could be due to less perceived need or fewer opportunities for training in these roles.

These findings highlight the importance of providing access to training and professional development opportunities focused on AI across all roles in an organization, not just those in higher positions or those directly involved in AI-related tasks. This could help ensure a more widespread understanding and utilization of AI across the organization.

Despite these efforts, many participants did not feel adequately prepared to utilize generative AI tools professionally. A notable 62.91% disagreed to some extent with the statement: “I feel adequately prepared to use generative AI tools in my professional work as a librarian,” underscoring the need for more effective training programs.

Interestingly, the areas identified for further training weren’t just about understanding the basics of AI. Participants showed a clear demand for advanced understanding of AI concepts and techniques (13.53%), familiarity with AI tools and applications in libraries (14.21%), and addressing privacy and data security concerns related to generative AI (14.36%). This suggests that librarians are looking to move beyond a basic understanding and are keen to engage more deeply with AI.

Preferred formats for professional development opportunities leaned towards remote and flexible learning opportunities, such as online courses or webinars (26.02%) and self-paced learning modules (22.44%). This preference reflects the current trend towards digital and remote learning, providing a clear direction for future training programs.

Notably, almost half of the participants (43.99%) rated the need for academic librarians to receive training on AI tools and applications within the next twelve months as ‘extremely important.’ This emphasis on urgency indicates a significant and immediate gap to be addressed.

In summary, a deeper analysis of the data reveals a landscape where academic librarians possess moderate to low confidence in understanding, discussing, and handling AI-related tasks, despite some exposure to professional development in AI. This finding indicates the need for more comprehensive, in-depth, and accessible AI training programs. By addressing these knowledge gaps, the library community can effectively embrace AI’s potential and navigate its challenges.

RQ 3 Perceptions

The comprehensive results of our survey, as illustrated in Table 7, offer a detailed portrait of librarians’ perceptions towards the integration of generative AI tools in library services and operations.

When considering the potential benefits of AI, the responses indicate a degree of ambivalence, with 35.88% choosing a neutral stance. However, when we combine the categories of those who ‘agree’ and ‘strongly agree,’ we see that a significant portion, 49.84%, view AI as beneficial to a certain extent. Similarly, on the question of the importance of investment in AI, there is a notable inclination towards agreement, with 46.87% agreeing that investment is important to some degree.

However, this optimism is juxtaposed with concerns about readiness. When asked how prepared they feel to adopt generative AI tools within the forthcoming year, 70.03% of respondents (those who ‘strongly disagree’ or ‘disagree’) admit a lack of preparedness. This suggests that despite recognizing the potential value of AI, there are considerable obstacles to be overcome before implementation becomes feasible.

The uncertainty surrounding AI’s impact on libraries in the short-term further illuminates this complexity. A significant proportion of librarians (36.09%) chose a neutral response when asked to predict the impact of AI on academic libraries within the next twelve months. Nonetheless, there is a considerable group (41.06% who ‘agree’ or ‘strongly agree’) who foresee significant short-term impact.

A key finding from the survey was the collective recognition of the urgency to address ethical and privacy issues tied to AI usage. In fact, 74.34% of respondents, spanning ‘agree’ and ‘strongly agree,’ underscored the urgent need to address potential ethical and privacy concerns related to AI, highlighting the weight of responsibility librarians feel in maintaining the integrity of their services in the age of AI (Figure 2).

The qualitative responses provide a rich understanding of the perceptions of generative AI among library professionals and the implications they foresee for the library profession. The responses were categorized into several key themes, each of which is discussed below with relevant quotes from the respondents.

Ethical and Privacy Concerns

A significant theme that emerged from the responses was the ethical and privacy concerns associated with the use of generative AI tools in libraries. Respondents expressed apprehension about potential misuse of data and violations of privacy. As one respondent noted, “Library leaders should not rush to implement AI tools without listening to their in-house experts and operational managers.” Another respondent cautioned, “We need to be cautious about adopting technologies or practices within our own workflows that pose significant ethical questions, privacy concerns.”

Need for Education and Training

The need for education and training on AI for librarians was another prevalent theme. Respondents emphasized the importance of understanding AI tools and their implications before implementing them. One respondent suggested: “quickly education on AI is needed for librarians. As with anything else, there will be early adopters and then a range of adoption over time.” Another respondent highlighted the need for an AI specialist, stating, “I also think it would be valuable to have an AI librarian, someone who can be a resource for the rest of the staff.”

Potential for Misuse

Respondents expressed concern about the potential for misuse of AI tools, such as generating false citations or over-reliance on AI systems. They emphasized the importance of critical thinking skills, and cautioned against replacing human judgment and learning processes with AI. As one respondent put it, “Critical thinking skills and learning processes are vital and should not be replaced by AI.” Another respondent warned: “there are potential risks from misuse such as false citations being provided or too much dependence on systems.”

Concerns about Implementation

Several respondents expressed doubts about the ability of libraries to quickly and effectively implement AI tools. They cited issues such as frequent updates and refinements to AI tools, the need for significant investment, and the potential for AI to be used in ways that do not benefit the library or its users. One respondent noted, “the concern I have with AI tools is the frequent updates and refinements that occur. For libraries with small staff size, it seems daunting to keep up.”

Role of AI in Libraries

Some respondents suggested specific ways in which AI could be used in libraries, such as for collection development, instruction, and answering frequently asked questions. However, they also cautioned against viewing AI as a panacea for all library challenges. One respondent stated: “using them for FAQs will be more useful than answering a complicated reference question.”

Concerns about AI’s Impact on the Profession

Some respondents expressed concern that the use of AI could lead to job displacement or a devaluation of the human elements of librarianship. They suggested that AI should be used to complement, not replace, human librarians. One respondent expressed that, “I could see a future where only top research institutions have human reference librarians as a concierge service.”

Need for Critical Evaluation

Respondents emphasized the need for critical evaluation of AI tools, including understanding their limitations and potential biases. They suggested that libraries should not rush to implement AI without fully understanding its implications. One respondent advised: “the framing of AI usage as a forgone conclusion is concerning. It’s a tool, not a solution, and should not be implemented without due consideration.”

AI Literacy

Some respondents suggested that libraries have a role to play in teaching AI literacy to students and other library users. They emphasized the importance of understanding how AI tools work and how to use them responsibly. One respondent stated: “I think we need to teach AI literacy to students.” Another respondent echoed this sentiment, saying, “it is essential that we prepare our students to use generative AI tools responsibly.”

The perceptions of generative AI among library professionals are multifaceted, encompassing both the potential benefits and challenges of these technologies. While there is recognition of the potential of AI to enhance library services, there is also a strong emphasis on the need for ethical considerations, education and training, critical evaluation, and responsible use of these tools. The implications for the library profession are significant, with concerns about job displacement, the need for new skills and roles, and the potential for changes in library practices and services. These findings highlight the need for ongoing dialogue and research on the use of generative AI in libraries.

While library employees acknowledge the potential advantages of AI in library services, they also express concerns regarding readiness, and emphasize the urgency to address ethical and privacy considerations. These findings indicate the need for support systems, training, and resources to address readiness gaps, alongside rigorous discussion, and guidelines to navigate ethical and privacy issues as libraries explore the possibilities of AI integration.

Discussions

The survey results cast light on the current state of artificial intelligence literacy, training needs, and perceptions within the academic library community. The findings reveal a landscape of recognition for the potential of AI technologies, yet, simultaneously, a lack of in-depth understanding and preparedness for their adoption.

A detailed examination of the data reveals that a considerable number of library professionals self-assess their understanding of AI as sitting around, or below, the middle. While this does suggest a basic level of familiarity with AI concepts and principles, it likely falls short of the proficiency required to navigate the rapidly evolving AI landscape confidently and competently. This gap in understanding holds implications for the library field as AI continues to infiltrate various sectors and increasingly permeates library services and operations.

Moreover, an analysis of the familiarity of library professionals with AI tools lends further credence to this call for more comprehensive AI education initiatives. An understanding of AI extends beyond mere theoretical comprehension—it necessitates hands-on familiarity with AI tools and the ability to use and apply them in practice. Direct interaction with AI technologies provides an avenue for library professionals to bolster their practical understanding and thus equip them to incorporate these tools into their work more effectively.

However, formulating training initiatives that address these gaps is a multifaceted task. The AI usage in libraries is as diverse as the scope of AI applications themselves. From customer service chatbots, and text or data mining tools, to advanced technologies like neural networks and deep learning systems—each offers unique applications and therefore requires distinct expertise and understanding. Accordingly, training programs must be flexible and comprehensive, encompassing the full range of potential AI applications while also delving deep enough to provide a solid grasp of each specific tool’s functionality and potential uses.

The study also sheds light on the varying degrees of understanding across different AI concepts. Participants generally exhibited a higher level of comprehension for simpler AI concepts. However, their understanding waned when it came to more complex concepts, often the bedrock of cutting-edge AI applications. This variation in comprehension underscores the need for a stratified approach to AI education. Such an approach could start with foundational concepts and gradually progress towards more advanced topics, providing a scaffold on which a deeper understanding of AI can be built.

Addressing the AI literacy gap in the library sector thus requires a concerted approach—one that offers comprehensive and layered educational strategies that bolster both theoretical understanding and practical familiarity with AI. The aim should not only be to impart knowledge, but to empower library professionals to confidently navigate the AI landscape, to adopt and adapt AI technologies in their work effectively and—crucially —responsibly. Through such training and professional development initiatives, libraries can harness the potential of AI, ensuring they continue to be at the forefront of technological advancements.

As the focus shifts to the professional use of AI tools in libraries, the data reveal that their adoption is not yet commonplace. The use of AI tools—such as text generation and research assistance—are most reported, reflecting the immediate utility these technologies offer to librarians. However, a significant proportion of participants do not frequently use AI tools, indicating barriers to adoption. These barriers could include a lack of understanding or familiarity with these tools, a perceived lack of necessity for their use, or limitations in resources necessary for implementation and maintenance. To overcome these barriers, the field may need more than just providing education and resources. Demonstrating the tangible benefits and efficiencies AI tools can bring to library work could play a pivotal role in their wider adoption.

The data show a strong enthusiasm among librarians for professional development related to AI. While introductory training modalities are popular, the findings reveal a demand for more advanced, hands-on training. This need aligns with the complexity and rapid evolution of AI technologies, which require a deeper understanding to be fully leveraged in library contexts.

Furthermore, the findings highlight the importance of ethical considerations and the potential benefits of fostering communities of practice in AI training. With the increasing integration of AI technology into library services, the issues related to AI ethics will likely become more complex. Proactively addressing these concerns through in-depth, focused training can help libraries continue to serve as ethical stewards of information. Communities of practice provide a platform for shared learning, mutual support, and the pooling of resources, equipping librarians to better navigate the intricacies of AI integration.

Importantly, the data show that the diversity in librarians’ roles and contexts necessitates a tailored approach to AI training. Libraries differ in their services, target audiences, resources, and strategic goals, and so do their AI training needs. A one-size-fits-all approach to AI training may fall short. Future AI training could therefore take these variations into account, offering specialized tracks or modules catering to specific roles or institutional contexts.

Likewise, the perceptions surrounding the use of generative AI tools in libraries are intricate and multifaceted. While the potential benefits of AI are acknowledged and the importance of investing in its implementation recognized, there is also a pronounced lack of readiness to adopt these tools. This readiness gap could stem from various factors, such as a lack of technical skills, insufficient funding, or institutional resistance. Future research should delve into these possibilities to better understand and address this gap.

Library professionals express uncertainty about the short-term implications of AI for libraries. This could reflect the novelty of these technologies and a lack of clear use cases, or it could echo the experiences of early adopters. The findings also emphasize a heightened sense of urgency in addressing the ethical and privacy concerns associated with AI technologies. These concerns underline the necessity for ongoing dialogue, education, and policy development around AI use in libraries.

Conclusions and Future Directions

The results reveal an intricate landscape of AI understanding, usage, and perception in the library field. While the benefits of AI tools are acknowledged, a comprehensive understanding and readiness to implement these technologies remain less than ideal. This reality underlines the pressing need for an investment in targeted educational strategies and ongoing professional development initiatives.

Crucially, the wide variance in AI literacy, understanding of AI concepts, and hands-on familiarity with AI tools among library professionals points towards the need for a stratified and tailored approach to AI education. Future training programs must aim beyond just knowledge acquisition—they must equip library professionals with the capabilities to apply AI technologies in their roles effectively, ethically, and responsibly. Ethical and privacy concerns emerged as significant considerations in the adoption of AI technologies in libraries. Our findings reinforce the crucial role that libraries have historically played, and must continue to play, in advocating for ethical information practices.

The readiness gap in AI adoption uncovered by the study suggests a disconnect between understanding the potential of AI and the ability to harness it effectively. This invites a deeper investigation into potential barriers, including technical proficiency, resource allocation, and institutional culture, among others.

Framework and Key Competencies

This study presents a framework for defining AI literacy in academic libraries, encapsulating seven key competencies:

• Understanding AI System Capabilities and Limitations: Recognizing what AI can and cannot do, knowing its strengths and weaknesses.
• Identifying and Evaluating AI Use Cases: Discovering and assessing potential AI applications in library settings.
• Utilizing AI Tools Effectively and Appropriately: Applying AI technologies in library operations.
• Critically Assessing AI Quality, Biases, and Ethics: Evaluating AI for accuracy, fairness, and ethical considerations.
• Engaging in Informed AI Discussions and Collaborations: Participating knowledgeably in conversations and cooperative efforts involving AI.
• Recognizing Data Privacy and Security Issues: Understanding and addressing concerns related to data protection and security in AI systems.
• Anticipating AI’s Impacts on Library Stakeholders: Preparing for how AI will affect library users and staff.

This multidimensional definition of AI literacy for libraries provides a foundation for developing comprehensive training programs and curricula. For instance, the need to understand AI system capabilities and limitations highlighted in the definition indicates that introductory AI education should provide a solid grounding in how common AI technologies like machine learning work, where they excel, and their constraints. This conceptual comprehension equips librarians to set realistic expectations when evaluating or implementing AI.

The definition also accentuates that gaining practical skills to use AI tools appropriately should be a core training component. Hands-on learning focused on identifying appropriate applications, utilizing AI technologies effectively, and critically evaluating outputs can empower librarians to harness AI purposefully.

Moreover, emphasizing critical perspectives and ethical considerations reflects that AI training for librarians should move beyond technical proficiency. Incorporating modules examining biases, privacy implications, misinformation risks, and societal impacts is key for fostering responsible AI integration.

Likewise, the collaborative dimension of the definition demonstrates that cultivating soft skills for productive AI discussions and teamwork should be part of the curriculum. AI literacy has an important social element that training programs need to nurture.

Overall, this definition provides a skills framework that can inform multipronged, context-sensitive AI training tailored to librarians’ diverse needs. It constitutes an actionable guide for developing AI curricula and professional development that advance both technical and social aspects of AI literacy.

Future Research

Based on the findings and limitations of the current study, the following are specific recommendations for future research:

• Longitudinal Studies: This study provides a snapshot of AI literacy among academic library employees at a specific point in time. Future research could conduct longitudinal studies to track changes in AI literacy over time, which would provide insights into the effectiveness of interventions and the evolution of AI literacy in the library profession.
• Comparative Studies: This study focused on academic library employees. Future research could conduct comparative studies to examine AI literacy among different types of library employees (e.g., public library employees, school library employees), or among library employees in different countries. Such studies could provide insights into the factors that influence AI literacy and the strategies that are effective in different contexts.
• Intervention Studies: This study identified the need for education and training on AI. Future research could design and evaluate interventions aimed at enhancing AI literacy among library employees. Such studies could provide evidence-based recommendations for the development of training programs and resources.
• Ethical Considerations: This study highlighted ethical concerns about the use of AI in libraries. Future research could delve deeper into these ethical issues, examining the perspectives of different stakeholders (e.g., library users, library administrators) and exploring strategies for addressing these concerns.
• Impact of AI on Library Services: This study explored library employees’ perceptions of the potential impact of AI on library services. Future research could examine the actual impact of AI on library services, assessing the effectiveness of AI in enhancing user experience, streamlining operations, and supporting learning.

By pursuing these avenues for future research, we can continue to deepen our understanding of AI literacy in the library profession, inform strategies for enhancing AI literacy, and promote the effective and ethical use of AI in libraries.

Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2021). Explicating AI literacy of employees at digital workplaces. IEEE Transactions on Engineering Management , 68(5), 1259–1271.

Cox, A. (2022). The ethics of AI for information professionals: Eight scenarios.  Journal of the Australian Library and Information Association , 71(3), 201–214.

Heck, T., Weisel, L., & Kullmann, S. (2019). Information literacy and its interplay with AI . In A. Botte, P. Libbrecht, & M. Rittberger (Eds.), Learning Information Literacy Across the Globe (pp. 129–131). https://doi.org/10.25656/01:17891

Hervieux, S., & Wheatley, A. (2021). Perceptions of artificial intelligence: A survey of academic librarians in Canada and the United States.  The Journal of Academic Librarianship , 47(1), 102270.

Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence , 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101

Lo, L. S. (2023a). An initial interpretation of the U.S. Department of Education’s AI report: Implications and recommendations for Academic Libraries. The Journal of Academic Librarianship , 49(5), 102761. https://doi.org/10.1016/j.acalib.2023.102761

Lo, L. S. (2023b). The art and science of prompt engineering: A new literacy in the information age. Internet Reference Services Quarterly , 27(4), 203–210. https://doi.org/10.1080/10875301.2023.2227621

Lo, L. S. (2023c). The clear path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship , 49(4), 102720. https://doi.org/10.1016/j.acalib.2023.102720

Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: artificial intelligence‐written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology , 74(5), 570–581. https://doi.org/10.1002/asi.24750

McKinsey & Company. (2023). The state of AI in 2023 : Generative AI’s breakout year . McKinsey & Company. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year

Mishra, P., & Koehler, M.J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record , 108(6), 1017–1054.

Mishra, P. (2019). Considering contextual knowledge: The TPACK diagram gets an upgrade. Journal of Digital Learning in Teacher Education , 35(2), 76–78. https://doi.org/10.1080/21532974.2019.1588611

Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence , 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041

Ocaña-Fernández, Y., Valenzuela-Fernández, L., & Garro-Aburto, L. (2019). Artificial intelligence and its implications in higher education. Propósitos y Representaciones , 7(2), 536–568. https://doi.org/10.20511/pyr2019.v7n2.274

Oliphant, T. (2015). Social media and web 2.0 in information literacy education in libraries: New directions for self-directed learning in the digital age. Journal of Information Literacy , 9(2), 37–49.

Pinski, M., & Benlian, A. (2023). AI literacy—Towards measuring human competency in artificial intelligence. Proceedings of the 56th Hawaii International Conference on System Sciences, 165–174. https://doi.org/10.24251/HICSS.2023.012

Ridley, M., & Pawlick-Potts, D. (2021). Algorithmic literacy and the role for libraries. Information Technology and Libraries , 40(2), 1–15. https://doi.org/10.6017/ital.v40i2.12963

Sobel, K., & Grotti, M.G. (2013). Using the TPACK framework to facilitate decision making on instructional technologies. Journal of Electronic Resources Librarianship , 25(4), 255–262. https://doi.org/10.1080/1941126X.2013.847671

UNESCO. (2021). AI and education: Guidance for policy-makers . United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000376709

U.S. Department of Education. (2023). (rep.). Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations . Retrieved from https://www2.ed.gov/documents/ai-report/ai-report.pdf .

Appendix A. Recruitment—Listservs

• American Indian Library Association (AILA)
• American Libraries Association (ALA) Members
• Asian Pacific American Librarians Association (APALA)
• □ Members
• □ University Libraries Section
• □ Distance and Online Learning Section
• □ Instruction Section
• Association of Research Libraries (ARL) Directors Listserv
• Black Caucus American Library Association (BCALA)
• Chinese American Librarians Association (CALA)
• Greater Western Library Alliance (GWLA) Directors’ listserv
• Minnesota Institute Graduates (MIECL)
• New Mexico Consortium of Academic Libraries (NMCAL) Directors’ Listserv

Appendix B. AI and Academic Librarianship

Survey flow.

Standard: Block 1 (1 Question)

Block: Knowledge and Familiarity (12 Questions)

Standard: Perceived Competence and Gaps in AI Literacy (5 Questions)

Standard: Training on Generative AI for Librarians (6 Questions)

Standard: Desired Use of Generative AI in Libraries (7 Questions)

Standard: Demographic (10 Questions)

Standard: End of Survey (1 Question)

Start of Block: Block 1

Q1.1 Introduction

Dr. Leo Lo from the University of New Mexico is conducting a research project. You are invited to participate in a research study aiming to assess AI literacy among academic library employees, identify gaps in AI literacy that require further professional development and training, and understand the differences in AI literacy levels across different roles and demographic factors. Before you begin the survey, please read this Informed Consent Form carefully. Your participation in this study is voluntary, and you may choose to withdraw at any time without any consequences.

Artificial Intelligence (AI) refers to the development of computer systems and software that can perform tasks that would typically require human intelligence. These tasks may include problem-solving, learning, understanding natural language, recognizing patterns, perception, and decision-making

You are being asked to participate based of the following inclusion and exclusion criteria:

Inclusion Criteria:

• Currently employed as an employee in a college or university library setting.
• Willing and able to provide informed consent for participation in the study.

The Exclusion Criteria are as Follows:

• Librarian employees working in non-academic library settings (e.g., public libraries, school libraries, special libraries).
• Individuals who are not currently library employees or who are employed in non-library roles within academic institutions.

The purpose of this study is to evaluate the current AI literacy levels of academic librarians and identify areas where further training and development may be needed. The findings will help inform the design of targeted professional development programs and contribute to the understanding of AI literacy in the library profession.

If you agree to participate in this study, you will be asked to complete an online survey that will take approximately 15–20 minutes to complete. The survey includes questions about your AI knowledge, familiarity with AI tools and applications, perceived competence in using AI, and your opinions on training needs.

Potential Risks and Discomforts

There are no known risks or discomforts associated with participating in this study. Some questions might cause minor discomfort due to self-reflection, but you are free to skip any questions you prefer not to answer. Benefits While there are no direct benefits to you for participating in this study, your responses will help contribute to a better understanding of AI literacy among academic librarians and inform the development of relevant professional training programs.

Confidentiality

Your responses will be anonymous, and no personally identifiable information will be collected. Data will be stored securely on password-protected devices or encrypted cloud storage services, with access limited to the research team. The results of this study will be reported in aggregate form, and no individual responses will be identifiable. Your information collected for this project will NOT be used or shared for future research, even if we remove the identifiable information like your name.

Voluntary Participation and Withdrawal

Your participation in this study is voluntary, and you may choose to withdraw at any time without any consequences. Please note that if you decide to withdraw from the study, the data that has already been collected from you will be kept and used. This is necessary to maintain the integrity of the study and ensure that the data collected is reliable and valid.

Contact Information

If you have any questions or concerns about this study, please contact the principal investigator, Leo Lo, at [email protected] . If you have questions regarding your rights as a research participant, or about what you should do in case of any harm to you, or if you want to obtain information or offer input, please contact the UNM Office of the IRB (OIRB) at (505) 277-2644 or irb.unm.edu

By clicking “I agree” below, you acknowledge that you have read and understood the information provided above, had an opportunity to ask questions, and voluntarily agree to participate.

I agree (1)

I do not agree (2)

Skip To: End of Survey If Q1.1 = I do not agree

End of Block: Block 1

Start of Block: Knowledge and Familiarity

Q2.1 Artificial Intelligence

(AI) refers to the development of computer systems and software that can perform tasks that would typically require human intelligence. These tasks may include problem-solving, learning, understanding natural language, recognizing patterns, perception, and decision-making

Please rate your overall understanding of AI concepts and principles (using a Likert scale, e.g., 1 = very low, 5 = very high)

Q2.2 On a scale of 1 to 5, how would you rate your understanding of generative AI ? (1 = not at all knowledgeable, 5 = extremely knowledgeable)

Q2.3 Rate your familiarity with generative AI tools (e.g., ChatGPT, DALL-E, etc.) (using a Likert scale, e.g., 1 = not familiar, 5 = very familiar)

Q2.4 Which of the following AI technologies or applications have you encountered or used in your role as an academic librarian? (Select all that apply)

• □ Chatbots (1)
• □ Text or data mining tools (2)
• □ Recommender systems (3)
• □ Image or object recognition (4)
• □ Automated content summarization (5)
• □ Sentiment analysis (6)
• □ Speech recognition or synthesis (7)
• □ Other(please specify) (8) __________________________________________________

Q2.5 For each of the following AI concepts, indicate your understanding of the concept by selecting the appropriate response.

Q2.6 Which of the following generative AI tools have you used at least a few times? (Select all that apply)

• □ Text generation (e.g., ChatGPT) (1)
• □ Image generation (e.g., DALL-E, Mid Journey) (2)
• □ Music generation (e.g., OpenAI’s MuseNet) (3)
• □ Video generation (e.g. Synthesia) (4)
• □ Presentation generation (e.g. Tome) (5)
• □ Voice generation (e.g. Murf) (6)
• □ Data synthesis for research purposes (7)
• □ Other (please specify) (8) __________________________________________________

Display This Question:

If If Which of the following generative AI tools have you used at least a few times? (Select all that a… q://QID5/SelectedChoicesCount Is Greater Than 0

Q2.7 Have you ever paid for a premium version of at least one of the AI tools (for example, ChatGPT Plus; or Mid Journey subscription plan, etc.)

Q2.8 How frequently do you use generative AI tools in your professional work? (Select one)

Several times per week (2)

A few times per month (4)

Monthly (5)

Less than once a month (6)

Q2.9 For what purposes do you use generative AI tools in your professional work? (Select all that apply)

• □ Content creation (e.g., blog posts, social media updates) (1)
• □ Research assistance (e.g., literature reviews, data synthesis) (2)
• □ Data analysis or visualization (3)
• □ Cataloging or metadata generation (4)
• □ User support or assistance (e.g., chatbots, virtual reference) (5)
• □ Other (please specify) (6) __________________________________________________

Q2.10 On a scale of 1 to 5, how would you rate how reliable  generative AI tools have been in fulfilling your professional needs? (1 = not at all reliable, 5 = extremely reliable) 

Please explain your choice. 

1 (1) __________________________________________________

2 (2) __________________________________________________

3 (3) __________________________________________________

4 (4) __________________________________________________

5 (5) __________________________________________________

Q2.11 What level of concern do you have for the following potential challenges in implementing generative AI technologies in academic libraries? (Rate each challenge on a scale of 1 to 5, where 1 = not at all concerned and 5 = extremely concerned)

Q2.12 How frequently do you use generative AI tools in your personal life ? (Select one)

End of Block: Knowledge and Familiarity

Start of Block: Perceived Competence and Gaps in AI Literacy

Q3.1 On a scale of 1 to 5, how confident are you in your ability to evaluate the ethical implications of using AI in your library? (1 = not at all confident, 5 = extremely confident)

Q3.2 On a scale of 1 to 5, how confident are you in your ability to participate in discussions about AI integration within your library? (1 = not at all confident, 5 = extremely confident)

Q3.3 On a scale of 1 to 5, how confident are you in your ability to collaborate with colleagues on AI-related projects in your library? (1 = not at all confident, 5 = extremely confident)

Q3.4 On a scale of 1 to 5, how confident are you in your ability to troubleshoot issues related to AI tools and applications used in your library? (1 = not at all confident, 5 = extremely confident)

Q3.5 On a scale of 1 to 5, how confident are you in your ability to provide guidance to library users about AI resources and tools ? (1 = not at all confident, 5 = extremely confident)

End of Block: Perceived Competence and Gaps in AI Literacy

Start of Block: Training on Generative AI for Librarians

Q4.1 Have you ever participated in any training or professional development programs focused on generative AI?

If Q4.1 = Yes

Q4.2 Please briefly describe the nature and content of the training or professional development program(s) you attended.

________________________________________________________________

Q4.3 To what extent do you agree or disagree with the following statement: “ I feel adequately prepared to use generative AI tools in my professional work as a librarian .” (1 = strongly disagree, 5 = strongly agree)

Q4.4 In which of the following areas do you feel the need for additional training or professional development related to AI? (Select all that apply)

• □ Basic understanding of AI concepts and terminology (1)
• □ Advanced understanding of AI concepts and techniques (2)
• □ Familiarity with AI tools and applications in libraries (3)
• □ Ethical considerations of AI in libraries (4)
• □ Collaborating on AI-related projects (5)
• □ Addressing privacy and data security concerns related to generative AI (6)
• □ Troubleshooting AI tools and applications (7)
• □ Providing guidance to library users about AI resources (8)
• □ Other (please specify) (9) __________________________________________________

Q4.5 What types of professional development opportunities related to AI would be most beneficial to you? (Select all that apply)

• □ Online courses or webinars (1)
• □ In-person workshops or seminars (2)
• □ Conference presentations or panel discussions (3)
• □ Self-paced learning modules (4)
• □ Mentoring or coaching (5)
• □ Peer learning groups or communities of practice (6)
• □ Other (please specify) (7) __________________________________________________

Q4.6 How important do you think it is for academic librarians to receive training on generative AI tools and applications in the next 12 months ? (1 = not at all important, 5 = extremely important)

End of Block: Training on Generative AI for Librarians

Start of Block: Desired Use of Generative AI in Libraries

Q5.1 To what extent do you agree or disagree with the following statement: “ I believe generative AI tools have the potential to benefit library services and operations .” (1 = strongly disagree, 5 = strongly agree)

Q5.2 How important do you think it is for your library to invest in the exploration and implementation of generative AI tools ? (1 = not at all important, 5 = extremely important)

Q5.3 If you have any additional thoughts or suggestions on how your library could or should use (or not use) generative AI tools, please share them here.

Q5.4 How soon do you think your library should prioritize implementing generative AI tools and applications? (Select one)

Immediately (1)

Within the next 6 months (2)

Within the next year (3)

Within the next 2–3 years (4)

More than 3 years from now (5)

Not a priority at all (6)

Q5.5 In your opinion, how prepared is your library to adopt generative AI tools and applications in the next 12 months? (1 = not at all prepared, 5 = extremely prepared)

Q5.6 To what extent do you think generative AI tools and applications will have a significant impact on academic libraries within the next 12 months ? (1 = no impact, 5 = major impact)

Q5.7 How urgent do you feel it is for your library to address the potential ethical and privacy concerns related to the use of generative AI tools and applications? (1 = not at all urgent, 5 = extremely urgent)

End of Block: Desired Use of Generative AI in Libraries

Start of Block: Demographic

Q6.1 In which type of academic institution is your library located? (Select one)

Community college (1)

College or university (primarily undergraduate) (2)

College or university (graduate and undergraduate) (3)

Research university (4)

Specialized or professional school (e.g., law, medical) (5)

Other (please specify) (6) __________________________________________________

Q6.2 Is your library an ARL member library?

Q6.3 Approximately how many students are enrolled at your institution? (Select one)

Fewer than 1,000 (1)

1,000–4,999 (2)

5,000–9,999 (3)

10,000–19,999 (4)

20,000–29,999 (5)

30,000 or more (6)

Q6.4 What is your current role or position in your organization? (Select one)

Senior management (e.g. Director, Dean, associate dean/director) (1)

Middle management (e.g. department head, supervisor, coordinator) (2)

Specialist or professional (e.g., librarian, analyst, consultant) (3)

Support staff or administrative (4)

Other (please specify) (5) __________________________________________________

Q6.5 In which area of academic librarianship do you primarily work? (Select one)

Administration or management (1)

Reference and research services (2)

Technical services (e.g., acquisitions, cataloging, metadata) (3)

Collection development and management (4)

Library instruction and information literacy (5)

Electronic resources and digital services (6)

Systems and IT services (7)

Archives and special collections (8)

Outreach, marketing, and communications (9)

Other (please specify) (10) __________________________________________________

Q6.6 How many years of experience do you have as a library employee?

Less than 1 year (1)

1–5 years (2)

6–10 years (3)

11–15 years (4)

16–20 years (5)

More than 20 years (6)

Q6.7 What is the highest level of education you have completed? (Select one)

High school diploma or equivalent (1)

Some college or associate degree (2)

Bachelor’s degree (3)

Master’s degree in library and information science (e.g., MLIS, MSLS) (4)

Master’s degree in another field (5)

Doctoral degree (e.g., PhD, EdD) (6)

Other (please specify) (7) __________________________________________________

Q6.8 What is your gender? (Select one)

Non-binary / third gender (3)

Prefer not to say (4)

Q6.9 What is your age range?

Under 25 (1)

65 and above (5)

Q6.10 How do you describe your ethnicity? (Select one or more)

• □ American Indian or Alaskan Native (1)
• □ Asian (2)
• □ Black or African American (3)
• □ Hawaiian or Other Pacific Islander (4)
• □ Hispanic or Latino (5)
• □ White (6)
• □ Prefer not to say (7)
• □ Other (8) __________________________________________________

End of Block: Demographic

Start of Block: End of Survey

Q7.1 Thank you for participating in our survey!

Your input is incredibly valuable to us and will contribute to our understanding of AI literacy among academic librarians. We appreciate the time and effort you have taken to share your experiences and opinions. The information gathered will help inform future professional development opportunities and address potential gaps in AI knowledge and skills.

We will carefully analyze the responses and share the findings with the academic library community. If you have any further comments or questions about the survey, please do not hesitate to contact us at [email protected].

Once again, thank you for your contribution to this important research. Your insights will help shape the future of AI in academic libraries.

Best regards,

University of New Mexico

End of Block: End of Survey

* Leo S. Lo is Dean, College of University Libraries and Learning Sciences at the University of New Mexico, email: [email protected] . ©2024 Leo S. Lo, Attribution-NonCommercial (https://creativecommons.org/licenses/by-nc/4.0/) CC BY-NC.

Article Views (Last 12 Months)

Contact ACRL for article usage statistics from 2010-April 2017.

Article Views (By Year/Month)

© 2024 Association of College and Research Libraries , a division of the American Library Association

Print ISSN: 0010-0870 | Online ISSN: 2150-6701

ALA Privacy Policy

ISSN: 2150-6701

• Search Menu
• Sign in through your institution
• Advance Articles
• Collections
• Focus Collections
• Teaching Tools in Plant Biology
• Browse by cover
• High-Impact Research
• Author Guidelines
• Quick and Simple Author Support
• Focus Issues Call for Papers
• Submission Site
• Open Access Options
• Self-Archiving Policy
• Why Publish with Us?
• About The Plant Cell
• About The American Society of Plant Biologists
• Editorial Board
• Advertising & Corporate Services
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

The lowdown on breakdown: open questions in plant proteolysis.

Authors are listed alphabetically (except for the lead author/coordinating editor). All authors contributed to writing and revising the article.

• Article contents
• Figures & tables
• Supplementary Data

Nancy A Eckardt, Tamar Avin-Wittenberg, Diane C Bassham, Poyu Chen, Qian Chen, Jun Fang, Pascal Genschik, Abi S Ghifari, Angelica M Guercio, Daniel J Gibbs, Maren Heese, R Paul Jarvis, Simon Michaeli, Monika W Murcha, Sergey Mursalimov, Sandra Noir, Malathy Palayam, Bruno Peixoto, Pedro L Rodriguez, Andreas Schaller, Arp Schnittger, Giovanna Serino, Nitzan Shabek, Annick Stintzi, Frederica L Theodoulou, Suayib Üstün, Klaas J van Wijk, Ning Wei, Qi Xie, Feifei Yu, Hongtao Zhang, The lowdown on breakdown: Open questions in plant proteolysis, The Plant Cell , 2024;, koae193, https://doi.org/10.1093/plcell/koae193

• Permissions Icon Permissions

Proteolysis, including post-translational proteolytic processing as well as protein degradation and amino acid recycling, is an essential component of the growth and development of living organisms. In this article, experts in plant proteolysis pose and discuss compelling open questions in their areas of research. Topics covered include the role of proteolysis in the cell cycle, DNA damage response, mitochondrial function, the generation of N-terminal signals (degrons) that mark many proteins for degradation (N-terminal acetylation, the Arg/N-degron pathway, and the chloroplast N-degron pathway), developmental and metabolic signaling (photomorphogenesis, abscisic acid and strigolactone signaling, sugar metabolism, and post-harvest regulation), plant responses to environmental signals (endoplasmic-reticulum associated degradation, chloroplast-associated degradation, drought tolerance, the growth-defense tradeoff)), and the functional diversification of peptidases. We hope these thought-provoking discussions help to stimulate further research.

Author notes

Email alerts, citing articles via.

• Recommend to Your Librarian
• Advertising & Corporate Services
• Awards & Funding
• Plant Science Today
• Plant Biology Meeting
• Meeting Management Services
• Plant Science Research Weekly
• Taproot: A Plantae Podcast

Affiliations

• Online ISSN 1532-298X
• Print ISSN 1040-4651
• Copyright © 2024 American Society of Plant Biologists
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Open access
• Published: 09 July 2024

Exploring the potential of artificial intelligence to enhance the writing of english academic papers by non-native english-speaking medical students - the educational application of ChatGPT

• Jiakun Li 1   na1 ,
• Hui Zong 1   na1 ,
• Erman Wu 1 , 4   na1 ,
• Rongrong Wu 1 ,
• Zhufeng Peng 1 ,
• Jing Zhao 1 ,
• Lu Yang 1 ,
• Hong Xie 2 &
• Bairong Shen 1 , 3  

BMC Medical Education volume  24 , Article number:  736 ( 2024 ) Cite this article

Metrics details

Academic paper writing holds significant importance in the education of medical students, and poses a clear challenge for those whose first language is not English. This study aims to investigate the effectiveness of employing large language models, particularly ChatGPT, in improving the English academic writing skills of these students.

A cohort of 25 third-year medical students from China was recruited. The study consisted of two stages. Firstly, the students were asked to write a mini paper. Secondly, the students were asked to revise the mini paper using ChatGPT within two weeks. The evaluation of the mini papers focused on three key dimensions, including structure, logic, and language. The evaluation method incorporated both manual scoring and AI scoring utilizing the ChatGPT-3.5 and ChatGPT-4 models. Additionally, we employed a questionnaire to gather feedback on students’ experience in using ChatGPT.

After implementing ChatGPT for writing assistance, there was a notable increase in manual scoring by 4.23 points. Similarly, AI scoring based on the ChatGPT-3.5 model showed an increase of 4.82 points, while the ChatGPT-4 model showed an increase of 3.84 points. These results highlight the potential of large language models in supporting academic writing. Statistical analysis revealed no significant difference between manual scoring and ChatGPT-4 scoring, indicating the potential of ChatGPT-4 to assist teachers in the grading process. Feedback from the questionnaire indicated a generally positive response from students, with 92% acknowledging an improvement in the quality of their writing, 84% noting advancements in their language skills, and 76% recognizing the contribution of ChatGPT in supporting academic research.

The study highlighted the efficacy of large language models like ChatGPT in augmenting the English academic writing proficiency of non-native speakers in medical education. Furthermore, it illustrated the potential of these models to make a contribution to the educational evaluation process, particularly in environments where English is not the primary language.

Peer Review reports

Introduction

Large language models (LLMs) are artificial intelligence (AI) tools that have remarkable ability to understand and generate text [ 1 , 2 ]. Trained with substantial amounts of textual data, LLMs have demonstrated their capability to perform diverse tasks, such as question answering, machine translation, and writing [ 3 , 4 ]. In 2022, Open AI released a LLM called ChatGPT [ 5 ]. Since its inception, ChatGPT has been widely applied in medicine domain, especially after testing, it can demonstrate the medical level that meets the requirements of passing the United States Medical Licensing Exam [ 6 ]. It can provide personalized learning experience according to the preference style of medical students [ 7 ]. Research has shown that the explanations provided by ChatGPT are more accurate and comprehensive than the explanations of basic principles provided in some standardized higher education exams [ 8 ]. Therefore, many researchers believe that ChatGPT may improve students’ problem-solving ability and reflective learning [ 9 ].

Writing English language based academic papers is very important for the development of medical students in universities. China is a non-native English-speaking country with a large population of medical students, so it is necessary to provide medical education and offer relevant courses, especially to cultivate their ability to write English academic papers [ 10 ]. This is essential for future engagement in scientific research and clinical work within the field of medicine. However, the ability of these non-native English-speaking medical students in writing English papers is relatively limited, and they need continuous training and improvement [ 11 ].

LLMs can be used to generate and modify text content and language styles, and can be applied to the quality improvement of scientific papers [ 12 , 13 ]. ChatGPT exhibits considerable potential in medical paper writing, assist in literature retrieval, data analysis, knowledge synthesis and other aspects [ 14 ]. Students received AI-assisted instruction exhibited improved proficiency in multiple aspects of writing, organization, coherence, grammar, and vocabulary [ 15 ]. Additionally, AI mediated instruction can positively impacts English learning achievement and self-regulated learning [ 16 ]. LLMs can also perform language translation [ 13 , 17 ]. Moreover, it can automatically evaluate and score the level of medical writing, and provide modification suggestions for improvement [ 18 ]. These studies indicate that incorporating large language models like ChatGPT into medical education holds promise for various advantages. However, their usage must be accompanied by careful and critical evaluation [ 19 ]. As far as we know, there is currently no research to evaluate the usability and effectiveness of ChatGPT in medical mini paper writing courses through real classroom teaching scenarios.

Therefore, in this study, we introduce the ChatGPT into real-world medical courses to investigate the effectiveness of employing LLMs in improving the academic writing proficiency for non-native English-speaking medical students. By collecting and analyzing data, we aim to provide evidence of the effectiveness of employing a LLM in improving the English academic writing skills of medical students, thereby facilitating better medical education and improve the scientific research ability and writing skills for students.

Participants

The research included 27 third-year medical students from the West China School of Medicine at Sichuan University. These students are all non-native English speakers. These students had concluded their fundamental medical coursework but had not yet embarked on specialized subjects. Exclusion criteria were applied to those who failed to fulfill the requisite homework assignments.

Initial Stage: The task involved composing an English academic paper in accordance with the stipulations of English thesis education. Considering the students’ junior academic standing, the composition of a discussion section in paper was not mandated. Each student was tasked with authoring a concise, “mini paper.”

Experimental Phase: Upon the completion of their individual “mini papers,” students had initially submitted these under the label “group without ChatGPT.” Subsequently, they engaged with ChatGPT-3.5 for a period of two weeks to refine their English academic manuscripts. After this period, the revised mini papers were resubmitted under the designation “group with ChatGPT.” Alongside this resubmission, students also provided a questionnaire regarding their experience with ChatGPT. The questionnaire was administered in Mandarin, which is the commonly used language in the research context. We conducted a thorough discussion within our teaching and research group to develop the questionnaire. Two students, who failed to meet the stipulated submission deadline, were excluded from the study.

All mini papers underwent evaluation and scoring based on a standardized scoring criterion. The assessment process encompassed three distinct approaches. Firstly, two teachers independently scored each mini paper using a blind review technique, and the final score was determined by averaging the two assessments. Secondly, scoring was performed using ChatGPT-3.5. Lastly, scoring was conducted using ChatGPT-4.

Evaluation Criteria: The scoring was composed of three dimensions: structure, logic, and language, with each dimension carrying a maximum of 20 points, culminating in a total of 60 points. The scores for each section were categorized into four tiers: 0–5 points (Fail), 6–10 points (Below Average), 11–15 points (Good), and 16–20 points (Excellent). The minimum unit for deduction was 0.5 points.

Structure emphasizes the organization and arrangement of the paper. It ensures that the content is placed in the appropriate sections according to the guidelines commonly found in academic journals. Logic refers to the coherence and progression of ideas within the paper. The logical flow should be evident, with each section building upon the previous ones to provide a cohesive argument. A strong logical framework ensures a systematic and well-supported study. Language refers to the correctness and proficiency of English writing. Proper language expression is essential for effectively conveying ideas and ensuring clear communication, and makes the paper becomes more readable and comprehensible to the intended audience.

Experience questionnaire for ChatGPT: The questionnaire comprised 31 questions, detailed in the attached appendix. (Attachment document)

Data analysis

The Kruskal-Wallis rank sum test was utilized to assess the baseline scores of students before and after using ChatGPT. A paired t-test was utilized to analyze the impact of ChatGPT on the improvement of students’ assignment quality (manual grading). Univariate regression analysis was conducted to investigate the extent of improvement in assignment quality attributed to ChatGPT. Previous studies have shown discrepancies in language learning and language-related skills between males and females. In order to mitigate any potential biases, we implemented gender correction techniques, which encompassed statistical adjustments to accommodate these gender variations [ 20 , 21 , 22 ]. The questionnaire was distributed and collected using the Wenjuanxing platform (Changsha Ran Xing Science and Technology, Shanghai, China. [ https://www.wjx.cn ]).

Statistical analyses were performed using the R software package (version 4.2.0, The R Foundation, Boston, MA, USA), Graph Pad Prism 9 (GraphPad Software, CA, USA), and Empower (X&Y Solutions Inc., Boston, MA, USA) [ 23 ].

Manual scoring

Ultimately, the study included 25 participants, with two students being excluded due to late submission of their assignments. These participants were all third-year undergraduate students, including 14 males (56%) and 11 females (44%). The “group without ChatGPT” consisted of 25 participants who wrote mini papers with an average word count of 1410.56 ± 265.32, cited an average of 16.44 ± 8.31 references, and received a manual score of 46.45 ± 3.59. In contrast, the “group with ChatGPT” of 25 participants produced mini papers with an average word count of 1406.52 ± 349.59, cited 16.80 ± 8.10 references on average, and achieved a manual score of 50.68 ± 2.03. Further details are available in Table  1 .

In terms of manual scoring, medical students demonstrated a significant improvement in the quality of their assignments in the dimensions of logic, structure, language, and overall score after using ChatGPT, as depicted in Fig.  1 .

Using ChatGPT improved the quality of students’ academic papers. A statistical analysis of the manual scoring showed that the quality of students’ academic papers improved after using ChatGPT for revision in terms of structure, logic, language, and overall score. The results showed statistical significance. *** p  < 0.001, **** p  < 0.0001

We also conducted a univariate analysis on the impact of ChatGPT on medical students’ academic papers writing across all scoring methods. The results indicated significant improvement in all manual scores and those evaluated by ChatGPT-3.5 for paper structure, logic, language, and total score (all p  < 0.05). Papers assessed by ChatGPT-4 also showed significant improvements in structure, logic, and total score (all p  < 0.05). Although the language scores of papers evaluated by ChatGPT-4 did not show a significant difference, a trend of improvement was observed (β 1.02, 95% confidence interval (CI) -0.15, 2.19, p  = 0.1). After adjusting for gender, multivariate regression analysis yielded similar results, with significant improvements in all dimensions of scoring across all methods, except for the language scores evaluated by ChatGPT-4. The total manual scoring of students’ papers improved by 4.23 (95% CI 2.64, 5.82) after revisions with ChatGPT, ChatGPT-3.5 scores increased by 4.82 (95% CI 2.47, 7.17), and ChatGPT-4 scores by 3.84 (95% CI 0.83, 6.85). Further details are presented in Table  2 .

The potential of ChatGPT in scoring support

Additionally, we investigated whether ChatGPT could assist teachers in assignment assessment. The results showed significant differences between the scores given by the ChatGPT-3.5 and manual grading, both for groups with and without ChatGPT. Interestingly, the scores from ChatGPT-4 were not significantly different from human grading, which suggests that ChatGPT-4 may have the potential to assist teachers in reviewing and grading student assignments (Fig.  2 ).

Potential of ChatGPT assisting teachers in evaluating papers. The results showed that there was a significant statistical difference between the scoring results of the GPT3.5 and the manual scoring results, both for the unrevised mini papers (left) and the revised mini papers (right) using ChatGPT. However, there was no significant statistical difference between the scoring results of GPT4 and the manual scoring results, which mean that GPT4 might be able to replace teachers in scoring in the future. ns: no significance, *** p  < 0.001, **** p  < 0.0001

Experience questionnaire

Among the 25 valid questionnaires, social media emerged as the primary channel through which participants became aware of ChatGPT, accounting for 84% of responses. This was followed by recommendations from acquaintances and requirements from schools/offices, each selected by 48% of participants. News media accounted for 44%. (Attachment document)

Regarding the purpose of using ChatGPT (multiple responses allowed), 92% used it mainly to enhance homework quality and improve writing efficiency. 68% utilized ChatGPT for knowledge gathering. 56% employed ChatGPT primarily to improve their language skills. (Attachment document)

In the course of the study, the most widely used feature of ChatGPT in assisting with academic paper writing was English polishing, chosen by 100% of the students, indicating its widespread use for improving the language quality of their papers. Generating outlines and format editing were also popular choices, with 64% and 60% using these features, respectively. (Attachment document)

When asked what they would use ChatGPT for, 92% of participants considered it as a language learning tool for real-time translation and grammar correction. 84% viewed ChatGPT as a tool for assisting in paper writing, providing literature materials and writing suggestions. 76% saw ChatGPT as a valuable tool for academic research and literature review. 48% believed that ChatGPT could serve as a virtual tutor, providing personalized learning advice and guidance. (Attachment document)

Regarding attitudes towards the role of ChatGPT in medical education, 24% of participants had an optimistic view, actively embracing its role, while 52% had a generally positive attitude, and 24% held a neutral stance. This indicates that most participants viewed the role of ChatGPT in medical education positively, with only a minority being pessimistic. (Attachment document)

Among the participants, when asked about the limitations of ChatGPT in medical education, 96% acknowledged the challenge in verifying the authenticity of information; 72% noted a lack of human-like creative thinking; 52% pointed out the absence of clinical practice insights; and 40% identified language and cultural differences as potential issues. (Attachment document)

The results from the participants’ two-week unrestricted usage of the AI model ChatGPT to enhance their assignments indicated a noticeable improvement in the quality of student papers. This suggests that large language models could serve as assistive tools in medical education by potentially improving the English writing skills of medical students. Furthermore, the results of comparative analysis revealed that the ChatGPT-4 model’s evaluations showed no statistical difference from teacher’s manual grading. Therefore, AI might have prospective applications in certain aspects of teaching, such as grading assessments, providing significant assistance to manual efforts.

The results of questionnaire indicate ChatGPT can serve as an important educational tool, beneficial in a range of teaching contexts, including online classroom Q&A assistant, virtual tutor and facilitating language learning [ 24 ]. ChatGPT’s expansive knowledge base and advanced natural language processing capability enable it to effectively answer students’ inquiries and offer valuable literature resources and writing advice [ 25 ]. For language learning, it offers real-time translation and grammar correction, aiding learners in improving their language skills through evaluation and feedback [ 26 ]. ChatGPT can also deliver personalized educational guidance based on individual student needs, enhancing adaptive learning strategies [ 27 ]. Furthermore, in this study, the positive feedback of questionnaire for the usage of ChatGPT in English language polishing of academic papers, as well as for generating paper outlines and formatting, underscores its acceptance and recognition among students. The evaluation results of three dimensions reflects a keen focus on enhancing the structural and formatting quality of their papers, demonstrating the large AI language model’s impressive teaching efficacy in undergraduate education.

In the questionnaire assessing ChatGPT’s accuracy and quality, 48% of respondents indicated satisfaction with its performance. However, it’s important to consider that the quality and accuracy of responses from any AI model, including ChatGPT, can be influenced by various factors such as the source of data, model design, and training data quality. These results, while indicative, require deeper research and analysis to fully understand the capabilities and limitations of ChatGPT in this field. Furthermore, ongoing discussions about ethics and data security in AI applications highlight the need for continued vigilance and improvement [ 28 ]. Overall, while ChatGPT shows promise in medical education, it is clear that it has limitations that must be addressed to better serve the needs of this specialized field.

Manual grading can be a time-consuming task for teachers, particularly when dealing with a large number of assignments or exams. ChatGPT-4 may provide support to teachers in the grading process, which could free up their time, allowing them to focus on other aspects of teaching, such as providing personalized feedback or engaging with students. However, it may not replace the role of teachers in grading. Teachers possess valuable expertise and contextual knowledge that go beyond simple evaluation of assignments. They consider factors such as student effort, creativity, critical thinking, and the ability to convey ideas effectively. These aspects might be challenging for an AI model to fully capture and evaluate. Furthermore, the use of AI in grading raises important ethical considerations. It is crucial to ensure that the model’s grading criteria align with educational standards and are fair and unbiased.

Despite its potential benefits of using ChatGPT in medical education, it also has limitations, such as language barriers and cultural differences [ 29 , 30 ]. When inputted with different languages, ChatGPT may have difficulty in understanding and generating accurate responses. Medical terms and concepts vary across different languages, and even slight differences in translation can lead to misunderstandings. Medical education is also influenced by cultural factors. Different cultures have different communication styles, which can impact the way medical information is exchanged. Recognizing and respecting the diversity of cultural perspectives is crucial for providing patient-centered care, and it should be an important part in medical education, which ChatGPT does not excel at. The model may struggle with translating non-English languages, impacting its effectiveness in a global medical education context. Additionally, while ChatGPT can generate a vast amount of text, it lacks the creative thinking and contextual understanding inherent to human cognition, which can be crucial in medical education. Another concern is the authenticity and credibility of the information generated by ChatGPT [ 31 , 32 ]. In medical education, where accuracy and reliability of knowledge are paramount, the inability to guarantee the truthfulness of the information poses a significant challenge [ 32 , 33 , 34 ].

These limitations of ChatGPT in medical education may be addressed and potentially rectified with updates and advancements in AI models. For instance, in this study, the scoring results showed no statistical difference between the ChatGPT-4 model and manual grading, unlike the significant discrepancies observed with the ChatGPT-3.5 model. This suggests that ChatGPT-4 has improved capabilities to assist manual grading by teachers, demonstrating greater intelligence and human-like understanding compared to the ChatGPT-3.5 model. Similar findings have been noted in other research, highlighting the advancements from version 3.5 to 4. For example, there were clear evidences that version 4 achieved better test results than version 3.5 in professional knowledge exams in disciplines such as orthopedics [ 35 ], dermatology [ 36 ], and ophthalmology [ 37 ].

This study aimed to explore the use of ChatGPT in enhancing English writing skills among non-native English-speaking medical students. The results showed that the quality of students’ writing improved significantly after using ChatGPT, highlighting the potential of large language models in supporting academic writing by enhancing structure, logic, and language skills. Statistical analysis indicated that ChatGPT-4 has the potential to assist teachers in grading. As a pilot study in this field, it may pave the way for further research on the application of AI in medical education. This new approach of incorporating AI into English paper writing education for medical students represents an innovative research perspective. This study not only aligns with the evolving landscape of technology-enhanced learning but also addresses specific needs in medical education, particularly in the context of academic writing. In the future, AI models should be more rationally utilized to further enhance medical education and improve medical students’ research writing skills.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, Scales N, Tanwani A, Cole-Lewis H, Pfohl S, et al. Large language models encode clinical knowledge. Nature. 2023;620(7972):172–80.

Article   Google Scholar  

Tamkin A, Brundage M, Clark J, Ganguli D. Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models. ArXiv 2021, abs/2102.02503.

Korngiebel DM, Mooney SD. Considering the possibilities and pitfalls of generative pre-trained transformer 3 (GPT-3) in healthcare delivery. NPJ Digit Med. 2021;4(1):93.

Zong H, Li J, Wu E, Wu R, Lu J, Shen B. Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses. BMC Med Educ. 2024;24(1):143.

ChatGPT. Optimizing Language Models for Dialogue [ https://openai.com/blog/chatgpt/ ]

Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepano C, Madriaga M, Aggabao R, Diaz-Candido G, Maningo J, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2(2):e0000198.

Sallam M. ChatGPT Utility in Healthcare Education, Research, and practice: systematic review on the promising perspectives and valid concerns. Healthc (Basel) 2023, 11(6).

Fijacko N, Gosak L, Stiglic G, Picard CT, John Douma M. Can ChatGPT pass the life support exams without entering the American heart association course? Resuscitation 2023, 185:109732.

Gilson A, Safranek CW, Huang T, Socrates V, Chi L, Taylor RA, Chartash D. How does ChatGPT perform on the United States Medical Licensing Examination (USMLE)? The implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Med Educ. 2023;9:e45312.

Wang W. Medical education in China: progress in the past 70 years and a vision for the future. BMC Med Educ. 2021;21(1):453.

Wu C, Zhang YW, Li AW. Peer feedback and Chinese medical students’ English academic writing development: a longitudinal intervention study. BMC Med Educ. 2023;23(1):578.

Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, Liu TY. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform 2022, 23(6).

Biswas S. ChatGPT and the future of Medical writing. Radiology. 2023;307(2):e223312.

Li J, Tang T, Wu E, Zhao J, Zong H, Wu R, Feng W, Zhang K, Wang D, Qin Y et al. RARPKB: a knowledge-guide decision support platform for personalized robot-assisted surgery in prostate cancer. Int J Surg 2024.

Song C, Song Y. Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students. Front Psychol. 2023;14:1260843.

Wei L. Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning. Front Psychol. 2023;14:1261955.

Panayiotou A, Gardner A, Williams S, Zucchi E, Mascitti-Meuter M, Goh AM, You E, Chong TW, Logiudice D, Lin X, et al. Language Translation Apps in Health Care settings: Expert Opinion. JMIR Mhealth Uhealth. 2019;7(4):e11316.

Veras M, Dyer JO, Rooney M, Barros Silva PG, Rutherford D, Kairy D. Usability and efficacy of Artificial Intelligence Chatbots (ChatGPT) for Health sciences students: protocol for a crossover randomized controlled trial. JMIR Res Protoc. 2023;12:e51873.

Jeyaraman M, K SP, Jeyaraman N, Nallakumarasamy A, Yadav S, Bondili SK. ChatGPT in Medical Education and Research: a Boon or a bane? Cureus 2023, 15(8):e44316.

Saxena S, Wright WS, Khalil MK. Gender differences in learning and study strategies impact medical students’ preclinical and USMLE step 1 examination performance. BMC Med Educ. 2024;24(1):504.

D’Lima GM, Winsler A, Kitsantas A. Ethnic and gender differences in first-year college students’ goal orientation, self-efficacy, and extrinsic and intrinsic motivation. J Educational Res. 2014;107(5):341–56.

Kusnierz C, Rogowska AM, Pavlova I. Examining gender differences, personality traits, academic performance, and motivation in Ukrainian and Polish students of Physical Education: a cross-cultural study. Int J Environ Res Public Health 2020, 17(16).

Empower, X&Y Solutions Inc. (, Boston MA) [ https://www.empowerstats.com ]

Futterer T, Fischer C, Alekseeva A, Chen X, Tate T, Warschauer M, Gerjets P. ChatGPT in education: global reactions to AI innovations. Sci Rep. 2023;13(1):15310.

Khan RA, Jawaid M, Khan AR, Sajjad M. ChatGPT - reshaping medical education and clinical management. Pak J Med Sci. 2023;39(2):605–7.

Deng J, Lin Y. The Benefits and Challenges of ChatGPT: An Overview. Frontiers in Computing and Intelligent Systems 2023.

Baidoo-Anu D, Owusu Ansah L. Education in the era of Generative Artificial Intelligence (AI): understanding the potential benefits of ChatGPT in promoting teaching and learning. SSRN Electron J 2023.

Preiksaitis C, Rose C. Opportunities, challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: scoping review. JMIR Med Educ. 2023;9:e48785.

Albrecht UV, Behrends M, Schmeer R, Matthies HK, von Jan U. Usage of multilingual mobile translation applications in clinical settings. JMIR Mhealth Uhealth. 2013;1(1):e4.

Beh TH, Canty DJ. English and Mandarin translation using Google Translate software for pre-anaesthetic consultation. Anaesth Intensive Care. 2015;43(6):792–3.

Google Scholar  

Haleem A, Javaid M, Singh RP. An era of ChatGPT as a significant futuristic support tool: a study on features, abilities, and challenges. BenchCouncil Trans Benchmarks Stand Evaluations 2023.

Haque MU, Dharmadasa I, Sworna ZT, Rajapakse RN, Ahmad H. I think this is the most disruptive technology: Exploring Sentiments of ChatGPT Early Adopters using Twitter Data. ArXiv 2022, abs/2212.05856.

Cooper G. Examining Science Education in ChatGPT: an exploratory study of Generative Artificial Intelligence. J Sci Edu Technol. 2023;32:444–52.

Yu C, Zong H, Chen Y, Zhou Y, Liu X, Lin Y, Li J, Zheng X, Min H, Shen B. PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data. Brief Bioinform 2024, 25(3).

Massey PA, Montgomery C, Zhang AS. Comparison of ChatGPT-3.5, ChatGPT-4, and Orthopaedic Resident performance on Orthopaedic Assessment examinations. J Am Acad Orthop Surg. 2023;31(23):1173–9.

Lewandowski M, Lukowicz P, Swietlik D, Baranska-Rybak W. An original study of ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Dermatology Specialty Certificate examinations. Clin Exp Dermatol; 2023.

Teebagy S, Colwell L, Wood E, Yaghy A, Faustina M. Improved performance of ChatGPT-4 on the OKAP examination: a comparative study with ChatGPT-3.5. J Acad Ophthalmol (2017). 2023;15(2):e184–7.

Download references

Acknowledgements

The authors gratefully thank Dr. Changzhong Chen, Chi Chen, and Xin-Lin Chen (EmpowerStats X&Y Solutions, Inc., Boston, MA) for providing statistical methodology consultation.

This work was supported by the National Natural Science Foundation of China (32070671 and 32270690), and the Fundamental Research Funds for the Central Universities (2023SCU12057). The authors gratefully thank Dr. Changzhong Chen, Chi Chen, and Xin-Lin Chen (EmpowerStats X&Y Solutions, Inc., Boston, MA) for providing statistical methodology consultation.

Author information

Jiakun Li, Hui Zong and Erman Wu contributed equally to this work.

Authors and Affiliations

Department of Urology and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China

Jiakun Li, Hui Zong, Erman Wu, Rongrong Wu, Zhufeng Peng, Jing Zhao, Lu Yang & Bairong Shen

West China Hospital, West China School of Medicine, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, China

Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China

Bairong Shen

Department of Neurosurgery, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China

You can also search for this author in PubMed   Google Scholar

Contributions

J.L., H.Z. and E.W. contributed equally as first authors of this manuscript. J.L., H.X. and B.S. were responsible for the conception and design of this study. J.L., E.W., R.W., J.Z., L.Y. and Z.P. interpreted the data. J.L., E.W., H.Z. and L.Y. were responsible for the data acquisition. J.L., H.Z. and E.W. wrote the first draft, interpreted the data, and wrote the final version of the manuscript. J.Z. was committed to the language editing of the manuscript. All authors critically revised the manuscript for important intellectual content and approved the final version of the manuscript. H.X. and B.S. contributed equally as the corresponding authors of this manuscript. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Hong Xie or Bairong Shen .

Ethics declarations

Ethic approval and consent to participate.

Was not required for this study because the research data were anonymised, and the Research Ethics Committee of West China Hospital of Sichuan University determined it was not necessary based on the study’s nature.

Consent for publication

Not applicable (NA).

Competing interests

The authors declare no competing interests.

AI use in the writing process

During the writing of this work the author(s) used generative AI and/or AI-assisted technologies for the purpose of English language polishing. The author(s) take responsibility for the content and intended meaning of this article.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Li, J., Zong, H., Wu, E. et al. Exploring the potential of artificial intelligence to enhance the writing of english academic papers by non-native english-speaking medical students - the educational application of ChatGPT. BMC Med Educ 24 , 736 (2024). https://doi.org/10.1186/s12909-024-05738-y

Download citation

Received : 04 April 2024

Accepted : 02 July 2024

Published : 09 July 2024

DOI : https://doi.org/10.1186/s12909-024-05738-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Large language model
• Medical education
• Medical english
• Academic writing

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Oxford University Press's Academic Insights for the Thinking World

AI attitudes and behaviour: researcher profiles [interactive]

Oxford Academic

Learn more about the world of academic publishing—from open access to peer review, accessibility to getting published—with our Publishing 101 series on the OUPblog.

• By Jessamine Hopkins , Tamsin Chamberlain , and Laura Richards
• July 8 th 2024

Researchers’ attitudes to AI vary significantly across career stage, subject area, and country. While 76% of researchers say they have used some form of AI tool in their research, our survey uncovered unexpected generational differences and polarised opinions on the impact of AI.

We recently conducted a  survey of over 2,000 researchers to hear directly from the research community about how they are reacting to and using AI in their work. A statistical cluster analysis identified eight groups illustrating the spectrum of attitudes, from ‘Challengers’ (those fundamentally against AI), through to ‘Pioneers’ (those fully embracing AI). Click on the image below to learn more about these profiles.

Featured image by  Cash Macanaya  on  Unsplash .

Jessamine Hopkins , Tamsin Chamberlain , and Laura Richards work at Oxford University Press.

• Editor's Picks
• Online products
• Publishing 101
• Series & Columns

Our Privacy Policy sets out how Oxford University Press handles your personal information, and your rights to object to your personal information being used for marketing to you or being processed as part of our business activities.

We will only use your personal information to register you for OUPblog articles.

Or subscribe to articles in the subject area by email or RSS

Related posts:

Recent Comments

There are currently no comments.

Leave a Comment

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

12 research interview questions (with examples and answers)

Last updated

4 July 2024

Reviewed by

Dazzle the interviewing team and land the job of your dreams by coming prepared to answer the most commonly asked research interview questions.

Read our article (which includes example answers to get your brain juices flowing) to ensure you put your best foot forward for your next research interview.

• What are research interview questions?

If you have set your sights on working in research, you will have to answer research interview questions during the hiring process.

Whether you are interested in working as a research assistant or want to land an academic or industry research position in your chosen field, confidently answering research interview questions is the best way to showcase your skills and land the job.

Designed to be open-ended, research interview questions give your interviewer a chance to:

Get a better understanding of your research experience

Explore your areas of research expertise

Determine if you and your research are a good fit for their needs

Assess if they have the required resources for you to conduct your research effectively

• 12 research interview questions (with answers)

If you want to crush an upcoming interview for a research position, practicing your answers to commonly asked questions is a great place to start.

Read our list of research interview questions and answers to help get into the pre-interview zone (and, hopefully, ensure you land that position!)

• General research questions

General research questions are typically asked at the start of the interview to give the interviewer a sense of your work, personality, experience, and career goals. 

They offer a great opportunity to introduce yourself and your skills before you deep-dive into your specific area of expertise.

What is your area of research expertise?

Interviewers will ask this common kickoff question to learn more about you and your interests and experience. Besides providing the needed information, you can use this question to highlight your unique skills at the beginning of your interview to set the tone.

Example answer

“My research focuses on the interaction between social media use and teenager mental well-being. I’ve conducted [X number] studies which have been published in [X publications]. I love studying this topic because not only is it a pressing modern issue, it also serves a commonly overlooked population that requires and deserves additional attention and support.”

Why are you interested in [X research topic]?

Another icebreaker, this question allows you to provide some context and backstory into your passion for research.

“After completing my undergraduate degree in mechanical engineering, I had the opportunity to work with my current mentor on their research project. After we conducted the first experiment, I had a million other questions I wanted to explore—and I was hooked. From there, I was fortunate enough to be taken on as an assistant by my mentor, and they have helped me home in on my specific research topic over the past [X years].”

What are your favorite and least favorite aspects of research?

Playing off the classic “What are your greatest strengths and weaknesses?” interview question, this research-specific option often appears in these types of interviews.

This can be a tricky question to answer well. The best way to approach this type of question is to be honest but constructive. This is your opportunity to come across as genuine as you talk about aspects of research that challenge you—because no one wants to hear you like everything about your work!

“My favorite part of research is speaking directly to people in our target demographic to hear about their stories and experiences. My least favorite part is the struggle to secure grants to support my work—though now I have done that process a few times, it is less daunting than when I started.”

• In-depth interview questions about your research

Once the interviewer has a basic understanding of you, they will transition into asking more in-depth questions about your work.

Regardless of your level of experience, this is the portion of the interview where you can dazzle your potential employer with your knowledge of your industry and research topic to highlight your value as a potential employee.

Where has your work been published?

As this is a straightforward question, make sure you have to hand every place your work has been published. If your work is yet to be published, mention potential future publications and any other academic writing you have worked on throughout your career.

“My research has been published in [X number of publications]. If you want to read my published work, I am happy to share the publication links or print you a copy.”

Tell us about your research process

Getting into the meat and potatoes of your work, this question is the perfect opportunity to share your working process while setting clear expectations for the support you will need.

Research is a collaborative process between team members and your employer, so being clear about how you prefer to work (while acknowledging you will need to make compromises to adjust to existing processes) will help you stand out from other candidates.

“Historically, I have worked alongside a team of researchers to devise and conduct my research projects. Once we determine the topic and gather the needed resources, I strive to be collaborative and open as we design the study parameters and negotiate the flow of our work. I enjoy analyzing data, so in most cases, I take the lead on that portion of the project, but I am happy to jump in and support the team with other aspects of the project as well.”

What sources do you use to collect your research data?

Depending on the type of research you conduct, this question allows you to deep-dive into the specifics of your data-collection process. Use this question to explain how you ensure you are collecting the right data, including selecting study participants, filtering peer-reviewed papers to analyze, etc.

“Because my research involves collecting qualitative data from volunteers, I use strict criteria to ensure the people I interview are within our target demographic. During the interview, which I like doing virtually for convenience, I use [X software] to create transcripts and pool data to make the analysis process less time-consuming.”

• Leadership research questions

Many research positions require employees to take on leadership responsibilities as they progress throughout their careers.

If this is the case for your job position, have strong answers prepared to the following questions to showcase your leadership and conflict-management skills.

Are you interested in becoming a research leader or manager?

Many research positions are looking for people with leadership potential to take on more responsibility as they grow throughout their careers. If you are interested in pursuing research leadership, use this question to highlight your leadership qualities.

“While I currently do not have much research leadership experience, I have worked with so many lovely mentors, and I would love the opportunity to fulfill that role for the next generation of academics. Because I am quite organized and attuned to the challenges of research, I would love the opportunity to take on leadership responsibilities over time.”

How do you handle workplace conflicts within a research team?

Workplace conflict is always present when working with a team, so it is a common topic for research interview questions.

Despite being tricky to navigate, this type of question allows you to show you are a team player and that you know how to handle periods of interpersonal stress. 

“When I'm directly involved in a disagreement with my team members, I do my best to voice my opinion while remaining respectful. I am trained in de-escalation techniques, so I use those skills to prevent the argument from getting too heated. If I am a bystander to an argument, I try to help other team members feel heard and valued while disengaging any big emotions from the conversation.”

How would you support and motivate a struggling researcher on your team?

Research is a team effort. Employers are looking for people who can work well in teams as a priority when hiring. Describing your ability to support and encourage your team members is essential for crushing your research interview.

“Working in research is hard—so I have had my fair share of offering and receiving support. When I have noticed someone is struggling, I do my best to offset their workload (provided I have the space to assist). Also, because I pride myself on being a friendly and approachable person, I do my best to provide a safe, open space for my team members if they want to talk or vent about any issues.”

• Future-oriented research questions

As the interview comes to a close, your interviewer may ask you about your aspirations in academia and research.

To seal the deal and leave a positive impression, these types of questions are the perfect opportunity to remind your interviewer about your skills, knowledge base, and passion for your work and future in research.

What other areas of research are you interested in exploring?

Many hiring research positions may require their researchers to be open to exploring alternative research topics. If this applies to your position, coming prepared with adjacent topics to your current studies can help you stand out.

“While my primary interests are with my area of study, I also am interested in exploring [X additional topics] related to my current work.”

Where do you see your research in 5, 10, or 20 years?

Your employer wants to see you are interested in and invested in growing your research career with them. To scope out your aspirations (and to show you are a good match for their needs), they may ask you to detail your future career goals.

“In five years, I would love to have at least two more published projects, particularly in [X publication]. Past that, as I mature in my research career, I hope to take on more leadership roles in the next 10 to 20 years, including running my own lab or being invited to speak at conferences in my chosen field.”

In an ideal world, what would your perfect research job look like?

As a fun hypothetical question, the “ideal world” inquiry allows you to get creative and specific about your wishes and aspirations. If you get asked this question, do your best not to limit yourself. Be specific about what you want; you never know, some of your wishes may already be possible to fulfill!

“In an ideal world, I would love to be the lead of my own research team. We would have our own working space, access to [X specific research tool] to conduct our research, and would be able to attend conferences within our field as keynote speakers.”

• Get ready to ace your next research interview

Now you’re ready to dazzle your interviewers and land the research job of your dreams. Prepare strong and competent answers after reading this article on the most common research interview questions.

Arriving prepared for your interview is a great way to reduce stress, but remember: Showcasing yourself and your passion for your research is the number one way to stand out from the other applicants and get the job.

Best of luck. You’ve got this!

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 6 February 2023

Last updated: 6 October 2023

Last updated: 5 February 2023

Last updated: 16 April 2023

Last updated: 9 March 2023

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 4 July 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next.

Users report unexpectedly high data usage, especially during streaming sessions.

Users find it hard to navigate from the home page to relevant playlists in the app.

It would be great to have a sleep timer feature, especially for bedtime listening.

I need better filters to find the songs or artists I’m looking for.

Log in or sign up

Get started for free