academic researcher

Academic Research

Raysonho/ Wikimedia Commons. Academic research can be intense, stimulating, and rewarding. But it is important to know that a research career involves many activities besides research. Scientists spend their time writing applications for funding to do research, as well as writing scientific papers to report the findings of their research. In addition, they spend time presenting their research in oral or poster form to other scientists at group meetings, institutional meetings, and scientific conferences; they also spend time teaching students about their field of study. A scientist's life is often full of tasks that need to be done and most scientists work very hard, but they also love what they do.

Fields of Study

• Clinical Scientist: David Fredricks
• Epidemiologist: Gloria Coronado
• Geneticist: Katie Peichel
• Clinical Research: Dana Panteleef
• Research Technician: Nanna Hansen

If you're interested in a general sense in academic research, the first thing to figure out is which field of research is best for you.

The fundamental task of research is asking questions. There are many areas of research in the life sciences, and they generally fall into three categories based on the types of questions that are asked and the tools that are used to answer the questions:

Basic Research

Clinical research, population-based research.

Basic researchers ask questions about how fundamental life processes work. Examples of questions include the following:

• What are the mechanisms that determine how and when cells divide?
• How do DNA mutations associated with a disease occur?
• How and why do cells age?
• How and why does one type of cell work differently from another type of cell?

Basic researchers usually work in laboratories with other scientists, usually with one faculty member leading a group of postdoctoral fellows, graduate students, and lab technicians who do most of the lab work. The hours can be very long and the work can be challenging, especially for graduate students and postdoctoral fellows. Basic researchers often ask their questions using model organisms, including yeast, worms, flies, fish, and mice.

• Scientific Recruiter: Scott Canavera
• Staff Scientist: Tom Paulson
• Shared Resources: Julie Randolph-Habecker
• Faculty Member: Wendy Leisenring

Clinical researchers ask questions about how disease occurs and how it can be cured in humans. Examples of questions include the following:

• How can we manipulate the body's immune system to improve treatment of a disease?
• How can we create a drug to improve disease survival?
• What are the long-term impacts of treatment on quality of life?

Clinical researchers work in laboratories that are very similar to basic researchers, but they often work with human tissue samples to ask their questions. Many clinical researchers find it rewarding to work on a question that may have an impact that they will eventually see come to fruition. At the same time, when you're working with human tissue, you usually have a limited amount of it so the risks of making a mistake that will lose your sample could be high. Clinical researchers will often collaborate with biostatisticians to best design and analyze their studies in order to yield the maximum amount of relevant information.

Population-based research is done by epidemiologists who ask questions to determine how diet, genetics, and lifestyle may influence the risk of disease. They ask these questions in one of two ways:

• by following a group of people over time and correlating exposure to who gets a disease;
• by asking a group of people with a disease about their lifestyle and diet choices and comparing the data to a randomly chosen group without the disease in order to look for differences between the two groups.

The types of questions they ask include the following:

• How can we best prevent teenagers from starting to smoke?
• Do some genetic variants place a person at greater risk for cancer?
• Do vitamins help prevent cancer?
• Does exposure to certain chemicals increase the risk of getting a particular disease?

Epidemiologists also collaborate with biostatisticians in order to design and analyze studies so they can get the most information from them. Rather than work in a lab, epidemiologists often need no more than a desk and computer. However, the interdisciplinary field of molecular epidemiology is changing this, and many epidemiologists ask questions about how a particular gene can influence disease risk, rather than, or in addition to, a lifestyle exposure.

Roles in Research

Faculty member.

Faculty members usually have Ph.D.'s or M.D.'s and have gone through graduate school or medical school followed by several years of being a postdoctoral fellow or medical resident. A faculty member is the leader of their own lab or work group and determines the direction of the research in their group. Most faculty members spend a good deal of their time writing grant proposals and manuscripts, reading research papers, reviewing colleagues' manuscripts and grant proposals, thinking and talking with others about their research to gain new ideas, and mentoring the people in their group.

Faculty positions are usually very competitive to get and are often a result of hard work over many years. However, most faculty members love what they do and wouldn't trade it for anything.

Research Scientist

Shared resource specialist, technician and other support staff, administrative positions.

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Introduction to Academic Research

What is Academic Research?

• Planning your Research
• Search Strategies
• Choosing Sources
• Choosing Databases
• Scholarly Sources
• Evaluating Websites
• Citing your Sources

Academic research involves a thorough investigation into what is known about a given topic. In most cases, you will be required to examine and analyze scholarly sources when completing your assignments (unless otherwise indicated by your instructor).  Scholarly sources help:

• Add depth to your understanding.
• Strengthen your argument.
• Reduce bias and misconceptions.

Research assignments are designed to help you think like a researcher and learn good research skills, such as selecting appropriate topics, identifying keywords, searching for information efficiently, and evaluating your sources. In this guide, we'll cover some of the key information and skills you need to know to succeed at Sheridan.

In this Guide

• Last Updated: Jun 21, 2024 3:48 PM
• URL: https://sheridancollege.libguides.com/academic-research

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What is Academic Research?

After completing this module you will be able to:

• recognize why information exists, who creates it, and how information of all kinds can be valuable, even when it’s biased.
• understand what scholarly research is, how to find it, how the process of peer-review works, and how it gets published.
• identify types of databases and understand why databases are critical for academic research

How to use this module

This module is organized into a number of pages. To navigate, you can either:

• use the “Previous” and “Next” buttons at the bottom of each page (suggested)

• follow the links in the “Contents” menu on the left side of the page

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Academic researcher

Academic researchers carry out original, high-level research that generates knowledge and progresses current understanding

As an academic researcher you'll apply your expertise and skills developed through study and research. You'll aim to publish papers on your work in peer-reviewed, well-respected journals and write reports, books or chapters of books on your specialist area of knowledge.

You're also likely to be involved in the teaching and supervision of university students and speaking at conferences.

A significant amount of your time will be spent on planning research, attending meetings with colleagues and contributing to the strategic direction of your department or group.

Working as an academic researcher is the result of a significant amount of education, with a dedication to a subject area that you have studied intensively.

Types of academic researcher

Academic researchers may be employed in the following roles:

• PhD student or researcher
• postdoctoral research associate or assistant
• research associate or fellow
• higher education lecturer, senior lecturer, professor or reader.

As academic researchers are mainly based in universities, you will likely be employed as higher education teaching staff and will also carry out research. Some highly sought after roles are purely research based, but even posts such as postdoctoral researcher often have some teaching element.

You may also work outside of academia, employed by a private company, a government department, a research institute, or an NGO. If you are employed by a research institute you may deliver teaching in the associated university and supervise PhD, Masters and undergraduate projects as part of your role. This is often a strong factor in helping universities to attract the best students to their academic programmes.

Responsibilities

As an academic researcher, you'll need to:

• carry out original, high-level individual and collaborative research
• organise your own time and budget effectively, including for off-site and overseas visits
• analyse large sets of data and information, drawing relevant conclusions
• work to deadlines as required by fund or grant holder
• work on feasibility studies or pilot projects prior to gaining funding for research
• prepare and deliver presentations at national and international conferences to large audiences
• prepare and write high quality papers for submission to peer-reviewed journals and conference proceedings
• participate in group meetings with other researchers and support staff
• apply for sources of external funding in addition to that provided by your employer
• undertake thorough and comprehensive literature reviews
• teach undergraduate and postgraduate students
• develop knowledge and skills relating to the latest techniques and applications relevant to your area of interest and deliver training in research techniques and methods to colleagues and students
• develop positive working relationships with internal and external contacts
• comply with all health and safety and ethics requirements for research activities
• plan and develop future research objectives and proposals
• supervise students undertaking masters and PhD level projects
• manage academic staff if working at a more senior level.
• Funded PhD students usually receive a tax-free stipend in the form of a scholarship, bursary or Research Council Grant, but funding is also often sourced from industrial partners with an interest in the research outcomes - particularly in the STEM disciplines. The amount usually ranges from £15,000 to £20,000.
• The UKRI have recently increased the minimum stipend they offer PhD students to £18,622.
• Extra money may be paid for teaching and tutorial activities and laboratory demonstrating.
• Postdoctoral researchers' salaries range from £27,000 to £44,000.
• Senior researchers and senior lecturers can earn salaries ranging from £32,000 to £50,000.
• Salaries continue to rise significantly in higher level positions such as professor, reader and dean, where salaries can be in excess of £100,000.

Figures are intended as a guide only.

Working hours

Working hours are usually advertised as being 35 to 40 hours per week. In reality you'll work longer hours as required, in order to complete projects and reach publication deadlines and targets. This will include evenings and weekends. Time away from home may be common, depending on the nature of your specialism - for example, to complete scientific fieldwork overseas.

Employers will consider requests for flexible working arrangements, including part-time employment and job sharing. Options for remote work are also becoming more commonplace.

Highly experienced and knowledgeable academic researchers may work freelance, completing numerous short-term contracts. Some employers allow staff to request a period of sabbatical leave, normally lasting three to 12 months. This is typically unpaid, but working freelance or writing a book can develop long-term career prospects.

What to expect

• High-quality research is crucial to higher education institutions, as it ensures funding. You will be under pressure to publish research and show you are an integral part of the department's success.
• The working environment will vary depending on your specialist area, especially while completing fieldwork. It could involve working in noisy, dirty and potentially dangerous environments, and will involve some travel around the UK and potentially overseas. This is in contrast to other aspects of the role, which involve a lot of time sitting in front of a computer in an office or at home, analysing data and results, and writing reports and papers. Being unable to obtain meaningful results can be frustrating, so resilience and a positive outlook are crucial.
• Teaching, tutorials and supervising laboratory sessions all require extensive preparation, which is often done on an evening at home.
• Although work can be intense, you can manage your own time and usually work on a flexible schedule.
• You may need to take on several postdoctoral researcher roles at different institutions, both in the UK and sometimes internationally, before you secure a permanent post.
• Positions within the private sector can offer more job security as they are less dependent on funding.

Qualifications

To have a successful, long-term career as an academic researcher, you'll need to gain a degree relevant to your area of interest, followed by further qualifications and experience. It's a highly competitive field to enter, so strong evidence of the necessary skills and experience is crucial.

This usually involves completing a Masters course followed by a PhD. As part of your PhD you'll be expected to write a thesis of between 60,000 and 90,000 words, outlining your research plan.

It's relatively common for graduates with a four year undergraduate Masters qualification, such as MMath or MSci, to progress straight onto a PhD. The fourth year usually comprises a substantial research project, accounting for 60% to 100% of the course, which can evidence research, analytical and other relevant skills.

Some academic researchers enter the role following a successful career in industry, after gaining significant experience and completing relevant professional qualifications. This is likely to occur in more vocational areas, and so the lack of a PhD need not be a barrier to success. However research intensive universities may still prefer to recruit applicants offering higher level research qualifications.

You'll need:

• a high level of intellectual ability, to plan and carry out research
• technical aptitude, to learn how to use new equipment and emerging technology
• organisation skills, to plan your workload, support team members and manage large sets of data
• interpersonal skills, to develop strong working relationships
• critical thinking to solve high level problems
• excellent teamwork skills
• concise and meaningful written communication skills for publishing work, conference proceedings and funding bids
• a strong passion for your discipline and motivation to continue learning, reach deadlines and targets
• strong IT skills and excellent data analysis and statistical knowledge
• excellent verbal communication skills, to present ideas and conclusions in lectures and presentations
• budgeting skills to ensure funding covers all aspects of the project
• flexibility and resilience, to keep going when research doesn't generate results in the expected timescale.

Work experience

As the usual route into a successful career as an academic researcher is via a relevant PhD, you need to focus on gaining research experience that will help you to achieve this as a next step. Funded summer research internships for undergraduates are available at universities around the UK and involve working alongside PhD students and experienced researchers.

Research internships are often open to students from any institution, with successful applicants often having achieved exceptional results in their pre-university qualifications and first year undergraduate assessments. These opportunities may be based in research institutes, universities or a combination of the two, and are an ideal opportunity to demonstrate your potential to a future supervisor and develop your network.

Similarly, industry-based summer internships in a research and development environment can also provide excellent experience and insights. Some academic researchers enter the role with significant industry experience, rather than a PhD, so you should explore all relevant options and apply accordingly.

Other routes in include starting in positions working on research projects for other people in positions such as research assistant or research fellow. This allows you to gain relevant experience in the field and get paid while you do.

Universities are the main employers of academic researchers. Research institutes also employ staff carrying out academic research. They're often associated with one or more universities, and other relevant organisations such as a charity or other research institute. They may be housed within a university or elsewhere, and university employees often work within a research institute as part of their role. 

Opportunities exist to work in both types of institution in the UK and overseas. 

Look for job vacancies at:

• THE unijobs

Universities and research institutes usually advertise vacancies on their own websites. Relevant publications and specialist journals are also useful, as are social media channels such as X (formerly Twitter) and LinkedIn. 

You can also discover future possibilities at academic events and conferences by networking with relevant contacts to discuss collaborative work and potential future funding opportunities.

Long and short-term opportunities also exist in charities, NGOs, think tanks, consultancies and government departments, as well as in private companies. Short-term work is usually carried out on a freelance basis, where you'll research a topic for a client of the organisation. These opportunities are open to those with significant experience in a specialist area and may be carried out while working in another role or as a main source of income. Some academic researchers appear as experts on news programmes and documentaries, and may be involved in writing articles for national and international news outlets.

Professional development

As an academic researcher in a university, you'll have access to a range of training courses to enhance your effectiveness in the role such as IT, report writing, using data and statistics, media training, effective leadership, research techniques, administration and funding application training. These may be delivered as stand-alone courses or as part of a coordinated training programme aimed at PhD students or early career researchers. You may also have access to mentoring schemes and shadowing opportunities.

As an academic researcher, you are responsible for your own professional development and are expected to identify areas of need to focus on.

Some universities will require you to undertake a Postgraduate Certificate in Higher Education (PGCHE). You can undertake a PGCHE through part-time study on your own campus, or you may need to attend elsewhere. Some UK universities offer a blended-learning option. The cost of the course is almost always covered by your own university if taught by your institution. If your university doesn’t offer their own PGCHE, there are usually agreements that cover the cost of doing the course elsewhere.

Career prospects

Delivering positive outcomes in early roles in this career area will give you the best chance of long-term success. This requires strong performance while you:

• write and publish research papers in high-quality, peer-reviewed journals in line with departmental targets
• present at conferences, lectures and other teaching responsibilities
• contribute to writing bids and applications for research funding
• develop collaborative relationships with staff at other institutions.

Taking on additional responsibility, along with being a supportive and enthusiastic colleague, will also help. As you progress you'll gain more leadership and strategic responsibilities, so take any opportunities that allow you to demonstrate and develop these skills.

As your knowledge and reputation develop, you may be able to access increasingly senior opportunities outside academia in freelance and consulting roles. For example, experienced academic researchers often appear on documentaries, and occasionally play a role in the planning and design of TV programmes and series.

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How to become a successful researcher at every stage of your career

November 16, 2020

By Sneha Mittal Sachdeva

Steps to building a successful research career – with a JACC webinar for physician-scientists

Pursuing a career in research can be daunting. Regardless of your field, it can be highly competitive, with challenges at every stage. These include the uncertainty of grants and fellowships, maintaining work-life balance, and  publishing in premium, high-impact journals opens in new tab/window .

For physician-scientists, the success rates for securing research grants has declined from 33 percent to 19 percent, while the number of grant applications has increased by 72 percent. However, with a roadmap for success, the path can provide personal and professional fulfillment and dynamism.

In this article – based on our webinar the  Journal of the American College of Cardiology opens in new tab/window  (JACC) – Dr. Valentin Fuster and Dr. Harlan Krumholtz share best practices to become a successful researcher at three stages of your career: early, mid-career and senior. While their advice is for physician-scientists, it can apply to people in all fields of research.

Webinar: How to become a successful researcher at every stage of your career

How to Become a Successful Researcher At Every Stage of Your Career (brighttalk.com) opens in new tab/window Join this  free webinar opens in new tab/window  with Valentin Fuster, MD, PhD, MACC, Editor-in-Chief of the  Journal of the American College of Cardiology (JACC) opens in new tab/window , and Harlan M. Krumholz, MD, SM, FACC, Director of the  Yale Center for Outcomes Research and Evaluation opens in new tab/window  at the Yale School of Medicine. They discuss how to maintain a successful physician-scientist career at three stages: early, mid-career and senior. They also suggest tips for grant receipts and talk about the importance of the mentor/mentee relationship and the need for creativity in grant submissions.

1. Identify the right research project

As a researcher, irrespective of the career stage, understand that you’re in constant competition to continue your research. To ensure that you’re working in the right direction, you can follow this step-by-step approach:

Identify your skills and resources: Identify the range of skills you currently have and your available resources. But don’t be afraid to think big!

Recognize the requirements: Next, recognize what kind of projects are you willing to do. Ask yourself if you are flexible, if you’re willing to take risks and if you can really choose and afford to be entrepreneurial in terms of the available opportunities for your project.

Research the topic: Read and learn from the existing literature around your research topic, demonstrate the rationale for selecting the topic and ensure you’ve completed the background research before finalizing your research topic.

Improve the likelihood of success: Identify what resources, skills, individuals and support can enhance the likelihood of your success.

Prioritize time: Estimate the amount of time required to complete the project vs your available time. Allocate your time carefully to important projects, and don’t underestimate the time, efforts and energy required for each project. If you’re a senior researcher, identify the opportunities for networking, learning and future opportunities, but take a calculated approach before taking on a new project.

Understanding the current scenario: Understand what projects your sponsors, funding teams or organization will pay you to do. Ask yourself if you can leverage the available opportunities to find a balance between what you want to do, what the world is interested in and the support you can expect to receive for the project.

Make a strong case: Do you understand what you’re doing and why you’re doing this? In a short description, try to write key compelling reasons why you should take the project, and only take on the project if the reasons are convincing.

Once you have clarity on the research project topic, ensure you put your energy and efforts toward making the project a success. Then take all your learnings to your next project.

2. Develop and nurture qualities of being a successful researcher.

Is a researcher born or created with dedication and hard work? Or is it a combination of both? The best researchers are curious by nature. Here are a few other qualities that predispose them for success:

Courage: The top quality of any successful researcher is the courage to ask the right questions, seek answers from peers, experts as well as literature and questioning how their project will make an impact. A successful researcher will fight the fall into the comfort zone and will understand the rewards of a life in science which can help him/her contribute to the world.

Persistence: When thinking about your research career growth, envision the position you would like to achieve and the journey you would like to take to reach that position. Even though sometimes the journey might not quite suit you, don’t quit, learn and improve as you go.

Determination & Resilience in the face of challenges: Everyone among the top successful researchers have faced challenges at one or more junctures of their life. Everyone faces difficult times when people don’t believe in them or doubt their capabilities. However, what made them stand apart was the resilience they displayed in the face of challenges. When times are hard, don’t quit easily because success only comes to those who work hard.

Self-motivation: Surround yourself by an environment where you see examples of success, where you see people you admire, people who inspire us to think about what we might aspire to be, who we want to be and how do we want to get there. Find colleagues who’re asking questions, trying to seek knowledge to improve people lives and don’t limit this search to people just in front of you, but look for opportunities across institutions and across borders.

3. Find a mentor for every stage of your life.

A mentor is someone who can provide guidance and support, accommodate and suit your individual needs and requirements, understand your aspirations and become an anchor for you at difficult stages of life. Regardless of the stage of your career, the role of a mentor is critically important in steering your interests and contributing to your growth.

You can have several mentors in your life based on your career stage; for example, a mentor to guide your thesis, a mentor who supports your career growth and a mentor who is an anchor for your life. A great mentor-mentee relationship is one where you have good chemistry and comfort. Mentorship doesn’t necessarily mean a mentor is supposed to tell you what needs to be done, but it’s a relationship where you can always seek guidance and supporting advice.

If you’re in early or middle stages of your career, find a mentor who is welcoming, supportive, encouraging and helps create or discover opportunities for your growth.

If you’re a senior researcher, contribute to society by discovering people with talent and encouraging them. Find the right triggers, understand talent, and support the people who have the right ingredients to become successful in their life.

4. Understand your talent and enhance it.

Understand your talents, skills and interests, and spend time enhancing these. You can ask yourself these key questions to help you grow in the right direction:

Self-discovery questions:

What are you trying to achieve in the next 5 years?

What are your strengths and weaknesses?

What projects keep me excited

Which strengths would you like to cultivate in the upcoming years?

Self-motivation:

What happens when things don’t work?

How can I keep myself motivated?

What are my contributions to the society?

What is the journey I would like to go through?

How can I achieve that big win?

How do I connect with people?

How do I motivate others around me?

How do others around me keep me motivated?

Research area of interest:

Do you enjoy working on new ground-breaking research or does your strength lie in enhancing the existing research?

How can you add value to your institution with your research?

Parting wisdom

At every stage of your career, remember to be a bold and creative problem solver. Ensure you thank the people who have made your journey important and memorable. Be satisfied with what you do, understand your talent and invest in them continuously. Begin with the end in mind. Your research is not the money, but the contribution you have made to the society and the impact you have had on your team. And most importantly don’t forget to enjoy each stage of your journey, learning lessons and striving towards becoming a better version of yourself each day.

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Sneha mittal sachdeva.

The academic researcher role: enhancing expectations and improved performance

• Published: 01 August 2012
• Volume 65 , pages 525–538, ( 2013 )

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This article distinguishes between six tasks related to the academic researcher role: (1) networking; (2) collaboration; (3) managing research; (4) doing research; (5) publishing research; and (6) evaluation of research. Data drawn from surveys of academic staff, conducted in Norwegian universities over three decades, provide evidence that the researcher role has become more demanding with respect to all sub-roles, and that academic staff have responded to increasing external and internal demands by enhancing their role performance.

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Kyvik, S. The academic researcher role: enhancing expectations and improved performance. High Educ 65 , 525–538 (2013). https://doi.org/10.1007/s10734-012-9561-0

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What does a researcher do?

Would you make a good researcher? Take our career test and find your match with over 800 careers.

What is a Researcher?

A researcher is trained to conduct systematic and scientific investigations in a particular field of study. Researchers use a variety of techniques to collect and analyze data to answer research questions or test hypotheses. They are responsible for designing studies, collecting data, analyzing data, and interpreting the results. Researchers may work in a wide range of fields, including science, medicine, engineering, social sciences, humanities, and many others.

To become a researcher, individuals usually need to obtain a graduate degree in their chosen field of study. They may also need to gain experience working as an assistant or intern in a research setting before becoming a full-fledged researcher. Researchers may work in academic or industrial settings, or they may work independently as consultants or freelance researchers. Regardless of the setting, researchers play a vital role in advancing knowledge and finding solutions to real-world problems.

What does a Researcher do?

Researchers are essential to the advancement of knowledge in various fields, including science, technology, medicine, social sciences, and humanities. Their work involves conducting systematic investigations to gather data, analyze it, and draw meaningful conclusions. Through their research, they can identify new problems and challenges, develop innovative solutions, and test hypotheses to validate theories.

Researchers also play a critical role in improving existing practices and policies, identifying gaps in knowledge, and creating new avenues for future research. They provide valuable insights and information that can inform decision-making, shape public opinion, and drive progress in society.

Duties and Responsibilities The duties and responsibilities of researchers can vary depending on the field of study and the type of research being conducted. However, here are some common duties and responsibilities that researchers are typically expected to fulfill:

• Develop research proposals: Developing a research proposal typically involves identifying a research question or problem, reviewing the relevant literature, selecting appropriate research methods and techniques, and outlining the expected outcomes of the research. Researchers must also ensure that their proposal aligns with the funding agency's objectives and guidelines.
• Conduct literature reviews: Literature reviews involve searching for and reviewing existing research papers, articles, books, and other relevant publications to identify gaps in knowledge and to build upon previous research. Researchers must ensure that they are using credible and reliable sources of information and that their review is comprehensive.
• Collect and analyze data: Collecting and analyzing data is a key aspect of research. This may involve designing and conducting experiments, surveys, interviews, or observations. Researchers must ensure that their data collection methods are valid and reliable, and that their analysis is appropriate and accurate.
• Ensure ethical considerations: Research ethics involve ensuring that the research is conducted in a manner that protects the rights, welfare, and dignity of all participants, as well as the environment. Researchers must obtain informed consent from human participants, ensure that animal research is conducted ethically and humanely, and comply with relevant regulations and guidelines.
• Communicate research findings: Researchers must communicate their research findings clearly and effectively to a range of audiences, including academic peers, policymakers, and the general public. This may involve writing research papers, presenting at conferences, and producing reports or other materials.
• Manage research projects: Managing a research project involves planning, organizing, and coordinating resources, timelines, and budgets to ensure that the project is completed on time and within budget. Researchers must ensure that they have the necessary resources, such as funding, personnel, and equipment, and that they are managing these resources effectively.
• Collaborate with others: Collaboration is an important aspect of research, and researchers often work with other researchers, academic institutions, funding agencies, and industry partners to achieve research objectives. Collaboration can help to facilitate the sharing of resources, expertise, and knowledge.
• Stay up-to-date with developments in their field: Research is an evolving field, and researchers must stay up-to-date with the latest developments and trends in their field to ensure that their research remains relevant and impactful. This may involve attending conferences, workshops, and seminars, reading academic journals and other publications, and participating in professional development opportunities.

Types of Researchers There are many types of researchers, depending on their areas of expertise, research methods, and the types of questions they seek to answer. Here are some examples:

• Basic Researchers: These researchers focus on understanding fundamental concepts and phenomena in a particular field. Their work may not have immediate practical applications, but it lays the groundwork for applied research.
• Applied Researchers: These researchers seek to apply basic research findings to real-world problems and situations. They may work in fields such as engineering, medicine, or psychology.
• Clinical Researchers: These researchers conduct studies with human subjects to better understand disease, illness, and treatment options. They may work in hospitals, universities, or research institutes.
• Epidemiologists : These researchers study the spread and distribution of disease in populations, and work to develop strategies for disease prevention and control.
• Social Scientists: These researchers study human behavior and society, using methods such as surveys, experiments, and observations. They may work in fields such as psychology, sociology, or anthropology.
• Natural Scientists: These researchers study the natural world, including the physical, chemical, and biological processes that govern it. They may work in fields such as physics, chemistry, or biology.
• Data Scientists : These researchers use statistical and computational methods to analyze large datasets and derive insights from them. They may work in fields such as machine learning, artificial intelligence, or business analytics.
• Policy Researchers: These researchers study policy issues, such as healthcare, education, or environmental regulations, and work to develop evidence-based policy recommendations. They may work in government agencies, think tanks, or non-profit organizations.

What is the workplace of a Researcher like?

The workplace of a researcher can vary greatly depending on the field and area of study. Researchers can work in a variety of settings, including academic institutions, government agencies, non-profit organizations, and private companies.

In academic settings, researchers often work in universities or research institutions, conducting experiments and analyzing data to develop new theories and insights into various fields of study. They may also teach courses and mentor students in their area of expertise.

In government agencies, researchers may work on projects related to public policy, health, and safety. They may be responsible for conducting research to support the development of new regulations or programs, analyzing data to assess the effectiveness of existing policies, or providing expertise on specific issues.

Non-profit organizations often employ researchers to study social and environmental issues, such as poverty, climate change, and human rights. These researchers may conduct surveys and collect data to understand the impact of various programs and initiatives, and use this information to advocate for policy changes or other interventions.

Private companies also employ researchers, particularly in industries such as technology and healthcare. These researchers may be responsible for developing new products, improving existing technologies, or conducting market research to understand consumer preferences and behaviors.

Regardless of the setting, researchers typically spend a significant amount of time conducting research, analyzing data, and communicating their findings through presentations, reports, and publications. They may also collaborate with other researchers or professionals in their field, attend conferences and workshops, and stay up-to-date with the latest research and developments in their area of expertise.

Frequently Asked Questions

Academic writer vs researcher.

An academic writer is someone who produces written material for academic purposes, such as research papers, essays, and other scholarly works. Academic writers may work as freelance writers, editors, or as staff writers for academic institutions or publishers.

On the other hand, a researcher is someone who conducts original research to generate new knowledge or validate existing knowledge. Researchers may work in academic settings, government agencies, private companies, or non-profit organizations. They typically design and execute experiments, surveys, or other data collection methods, analyze the data, and draw conclusions based on their findings.

While there may be some overlap between the skills required for academic writing and research, they are distinct activities with different goals. Academic writers often rely on the research of others to support their arguments, while researchers generate new knowledge through their own experiments and data analysis. However, academic writers may also be researchers who write about their own research findings.

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Academic Research Home: What is Academic Research? Why is it Important?

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What is Academic Research

Professors and others in academic fields often conduct research related to their studies. These researchers may be scientists, sociologists, educators, historians, English professors, etc. When conduct experiments or conduct a systemic analysis they then write an article with their findings.

This article is then submitted to a journal for review. This is called the peer review process (see below). Once approved, the article is published in the journal.

These articles are sometimes referred to as scholarly research, journal article, or peer reviewed article.

To be considered academic research, the article should include a discussion of the research methods, a detailed summary of the data, and an analysis of the data. Look for the following sections: (the section names may differ) Abstract, Methods, Data, Conclusion. More information about what these sections mean can be found on the " How to Read " page. 

This type of research is important because it provides new information for those in the field. These articles are often the primary source of information in the sciences. It also helps inform best practices or analyzes current systems.

Peer Review Process

The peer review process.

The peer review process describes the process in which academic research is approved for publication.

After conducting research, the researcher (or more often than not, researchers) write an article which discusses their guiding question or hypothesis, their methods to conduct the research, their findings, and their analysis of the findings.

The research then submits the article to a journal for publication. Before the article is published, a panel of peers in with that specific academic expertise critically reviews the article. They ensure that the research methods are based on sound methods, that the results match the method of research, and that the conclusions drawn by the research are valid. They then either approve the article for publication, request revisions, or deny the article for publication.

Research and Identiies

Is it credible?

Peer reviewed academic research is often considered one of the most credible reference source. The peer review process is rigorous, and misleading or false information or conclusions is often caught before publication.

HOWEVER, nothing is perfect. Mistakes are sometimes missed, and fraudulent data is occasionally published. Additionally, bias exists in any academic field, and that bias can affect all levels of the peer review process (the question being researched, their research methods, the conclusions, and the peer review response). So, as with any source, it is still important to read with a critical eye.

Other Types of Articles

Other articles in academic journals.

Academic journals typically publish this kind of peer-reviewed research, but they might also publish any of the following types of articles as well.

• Literature review - a review of previously published research on a subject
• Meta-analysis - an analysis of previously published research
• Book review
• Editorial or commentary
• Conference reports

These articles are often great as a source for your school research, but if your teacher requests an academic research article, it is important to make sure includes sections that discuss methods, data, and analysis.

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Academic researcher

An academic researcher is a type of researcher associated with a university or medical professional school (e.g., medical school or dental school). In medical research, an academic researcher may have a PhD, MD, or both, or they may have another type of medical, allied health, or social science related degree. Many academic medical researchers, but not all, lead research laboratories, treat patients clinically, and teach and train medical and scientific research students.

Learn More NCATS Toolkit for Patient-Focused Therapy Development: With Academic Researchers and Clinicians NCATS Toolkit for Patient-Focused Therapy Development: Organize In-Person Scientific Meetings

Critical Thinking in Academic Research - Second Edition

(4 reviews)

Cindy Gruwell, University of West Florida

Robin Ewing, St. Cloud State University

Copyright Year: 2022

Last Update: 2023

Publisher: Minnesota State Colleges and Universities

Language: English

Formats Available

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Reviewed by Julie Jaszkowiak, Community Faculty, Metropolitan State University on 12/22/23

Organized in 11 parts, this his textbook includes introductory information about critical thinking and details about the academic research process. The basics of critical thinking related to doing academic research in Parts I and II. Parts III –... read more

Comprehensiveness rating: 5 see less

Organized in 11 parts, this his textbook includes introductory information about critical thinking and details about the academic research process. The basics of critical thinking related to doing academic research in Parts I and II. Parts III – XI provide specifics on various steps in doing academic research including details on finding and citing source material. There is a linked table of contents so the reader is able to jump to a specific section as needed. There is also a works cited page with information and links to works used for this textbook.

Content Accuracy rating: 5

The content of this textbook is accurate and error free. It contains examples that demonstrate concepts from a variety of disciplines such as “hard science” or “popular culture” that assist in eliminating bias. The authors are librarians so it is clear that their experience as such leads to clear and unbiased content.

Relevance/Longevity rating: 5

General concepts about critical thinking and academic research methodology is well defined and should not become obsolete. Specific content regarding use of citation tools and attribution structure may change but the links to various research sites allow for simple updates.

Clarity rating: 5

This textbook is written in a conversational manner that allows for a more personal interaction with the textbook. It is like the reader is having a conversation with a librarian. Each part has an introduction section that fully defines concepts and terms used for that part.

Consistency rating: 5

In addition to the written content, this textbook contains links to short quizzes at the end of each section. This is consistent throughout each part. Embedded links to additional information are included as necessary.

Modularity rating: 4

This textbook is arranged in 11 modular parts with each part having multiple sections. All of these are linked so a reader can go to a distinct part or section to find specific information. There are some links that refer back to previous sections in the document. It can be challenging to return to where you were once you have jumped to a different section.

Organization/Structure/Flow rating: 5

There is clear definition as to what information is contained within each of the parts and subsequent sections. The textbook follows the logical flow of the process of researching and writing a research paper.

Interface rating: 4

The pictures have alternative text that appears when you hover over the text. There is one picture on page 102 that is a link to where the downloaded picture is from. The pictures are clear and supportive of the text for a visual learner. All the links work and go to either the correct area of the textbook or to a valid website. If you are going to use the embedded links to go to other sections of the textbook you need to keep track of where you are as it can sometimes get confusing as to where you went based on clicking links.

Grammatical Errors rating: 4

This is not really a grammatical error but I did notice on some of the quizzes if you misspelled a work for fill in the blank it was incorrect. It was also sometimes challenging to come up with the correct word for the fill in the blanks.

Cultural Relevance rating: 5

There are no examples or text that are culturally insensitive or offensive. The examples are general and would be applicable to a variety of students study many different academic subjects. There are references and information to many research tools from traditional such as checking out books and articles from the library to more current such as blogs and other electronic sources. This information appeals to a wide expanse of student populations.

I really enjoyed the quizzes at the end of each section. It is very beneficial to test your knowledge and comprehension of what you just read. Often I had to return and reread the content more critically based on my quiz results! They are just the right length to not disrupt the overall reading of the textbook and cover the important content and learning objectives.

Reviewed by Sara Stigberg, Adjunct Reference Librarian, Truman College, City Colleges of Chicago on 3/15/23

Critical Thinking in Academic Research thoroughly covers the basics of academic research for undergraduates, including well-guided deeper dives into relevant areas. The authors root their introduction to academic research principles and practices... read more

Critical Thinking in Academic Research thoroughly covers the basics of academic research for undergraduates, including well-guided deeper dives into relevant areas. The authors root their introduction to academic research principles and practices in the Western philosophical tradition, focused on developing students' critical thinking skills and habits around inquiry, rationales, and frameworks for research.

This text conforms to the principles and frames of the Framework for Information Literacy for Higher Education, published by the Association of College and Research Libraries. It includes excellent, clear, step-by-step guides to help students understand rationales and techniques for academic research.

Essential for our current information climate, the authors present relevant information for students who may be new to academic research, in ways and with content that is not too broad or too narrow, or likely to change drastically in the near future.

The authors use clear and well-considered language and explanations of ideas and terms, contextualizing the scholarly research process and tools in a relatable manner. As mentioned earlier, this text includes excellent step-by-step guides, as well as illustrations, visualizations, and videos to instruct students in conducting academic research.

(4.75) The terminology and framework of this text are consistent. Early discussions of critical thinking skills are tied in to content in later chapters, with regard to selecting different types of sources and search tools, as well as rationales for choosing various formats of source references. Consciously making the theme of critical thinking as applied to the stages of academic research more explicit and frequent within the text would further strengthen it, however.

Modularity rating: 5

Chapters are divided in a logical, progressive manner throughout the text. The use of embedded links to further readings and some other relevant sections of the text are an excellent way of providing references and further online information, without overwhelming or side-tracking the reader.

Topics in the text are organized in logical, progressive order, transitioning cleanly from one focus to the next. Each chapter begins with a helpful outline of topics that will be covered within it.

There are no technical issues with the interface for this text. Interactive learning tools such as the many self-checks and short quizzes that are included throughout the text are a great bonus for reinforcing student learning, and the easily-accessible table of contents was very helpful. There are some slight inconsistencies across chapters, however, relative to formatting images and text and spacing, and an image was missing in the section on Narrowing a Topic. Justifying copy rather than aligning-left would prevent hyphenation, making the text more streamlined.

Grammatical Errors rating: 5

(4.75) A few minor punctuation errors are present.

The authors of this text use culturally-relevant examples and inclusive language. The chapter on Barriers to Critical Thinking works directly to break down bias and preconceived notions.

Overall, Critical Thinking in Academic Research is an excellent general textbook for teaching the whys and hows of academic research to undergraduates. A discussion of annotated bibliographies would be a great addition for future editions of the text. ---- (As an aside for the authors, I am curious if the anonymous data from the self-checks and quizzes is being collected and analyzed for assessment purposes. I'm sure it would be interesting!)

Reviewed by Ann Bell-Pfeifer, Program Director/ Instructor, Minnesota State Community and Technical College on 2/15/23

The book has in depth coverage of academic research. A formal glossary and index were not included. read more

Comprehensiveness rating: 4 see less

The book has in depth coverage of academic research. A formal glossary and index were not included.

The book appears error free and factual.

The content is current and would support students who are pursuing writing academic research papers.

Excellent explanations for specific terms were included throughout the text.

The text is easy to follow with a standardized format and structure.

The text contains headings and topics in each section.

It is easy to follow the format and review each section.

Interface rating: 5

The associated links were useful and not distracting.

No evidence of grammatical errors were found in the book.

The book is inclusive.

The book was informative, easy to follow, and sequential allowing the reader to digest each section before moving into another.

Reviewed by Jenny Inker, Assistant Professor, Virginia Commonwealth University on 8/23/22

This book provides a comprehensive yet easily comprehensible introduction to critical thinking in academic research. The author lays a foundation with an introduction to the concepts of critical thinking and analyzing and making arguments, and... read more

This book provides a comprehensive yet easily comprehensible introduction to critical thinking in academic research. The author lays a foundation with an introduction to the concepts of critical thinking and analyzing and making arguments, and then moves into the details of developing research questions and identifying and appropriately using research sources. There are many wonderful links to other open access publications for those who wish to read more or go deeper.

The content of the book appears to be accurate and free of bias.

The examples used throughout the book are relevant and up-to-date, making it easy to see how to apply the concepts in real life.

The text is very accessibly written and the content is presented in a simple, yet powerful way that helps the reader grasp the concepts easily. There are many short, interactive exercises scattered throughout each chapter of the book so that the reader can test their own knowledge as they go along. It would be even better if the author had provided some simple feedback explaining why quiz answers are correct or incorrect in order to bolster learning, but this is a very minor point and the interactive exercises still work well without this.

The book appears consistent throughout with regard to use of terminology and tone of writing. The basic concepts introduced in the early chapters are used consistently throughout the later chapters.

This book has been wonderfully designed into bite sized chunks that do not overwhelm the reader. This is perhaps its best feature, as this encourages the reader to take in a bit of information, digest it, check their understanding of it, and then move on to the next concept. I loved this!

The book is organized in a manner that introduces the basic architecture of critical thinking first, and then moves on to apply it to the subject of academic research. While the entire book would be helpful for college students (undergraduates particularly), the earlier chapters on critical thinking and argumentation also stand well on their own and would be of great utility to students in general.

This book was extremely easy to navigate with a clear, drop down list of chapters and subheadings on the left side of the screen. When the reader clicks on links to additional material, these open up in a new tab which keeps things clear and organized. Images and charts were clear and the overall organization is very easy to follow.

I came across no grammatical errors in the text.

Cultural Relevance rating: 4

This is perhaps an area where the book could do a little more. I did not come across anything that seemed culturally insensitive or offensive but on the other hand, the book might have taken more opportunities to represent a greater diversity of races, ethnicities, and backgrounds.

This book seems tailor made for undergraduate college students and I would highly recommend it. I think it has some use for graduate students as well, although some of the examples are perhaps little basic for this purpose. As well as using this book to guide students on doing academic research, I think it could also be used as a very helpful introduction to the concept of critical thinking by focusing solely on chapters 1-4.

Table of Contents

• Introduction
• Part I. What is Critical Thinking?
• Part II. Barriers to Critical Thinking
• Part III. Analyzing Arguments
• Part IV. Making an Argument
• Part V. Research Questions
• Part VI. Sources and Information Needs
• Part VII. Types of Sources
• Part VIII. Precision Searching
• Part IX. Evaluating Sources
• Part X. Ethical Use and Citing Sources
• Part XI. Copyright Basics
• Works Cited
• About the Authors

Ancillary Material

About the book.

Critical Thinking in Academic Research - 2nd Edition provides examples and easy-to-understand explanations to equip students with the skills to develop research questions, evaluate and choose the right sources, search for information, and understand arguments. This 2nd Edition includes new content based on student feedback as well as additional interactive elements throughout the text.

About the Contributors

Cindy Gruwell is an Assistant Librarian/Coordinator of Scholarly Communication at the University of West Florida. She is the library liaison to the department of biology and the College of Health which has extensive nursing programs, public health, health administration, movement, and medical laboratory sciences. In addition to supporting health sciences faculty, she oversees the Argo IRCommons (Institutional Repository) and provides scholarly communication services to faculty across campus. Cindy graduated with her BA (history) and MLS from the University of California, Los Angeles and has a Masters in Education from Bemidji State University. Cindy’s research interests include academic research support, publishing, and teaching.

Robin Ewing is a Professor/Collections Librarian at St. Cloud State University. Robin is the liaison to the College of Education and Learning Design. She oversees content selection for the Library’s collections. Robin graduated with her BBA (Management) and MLIS from the University of Oklahoma. She also has a Masters of Arts in Teaching from Bemidji State University. Robin’s research interests include collection analysis, assessment, and online teaching.

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Academic Research: What it is + Free Tools

Academic research is critical to the success of a university, involving the whole system participating in it, whether it’s students, faculty members, or administrators. Although research is stereotypically associated with being in a laboratory mixing substances, the reality is that academic research involves all disciplines.

As a university student, you probably have many subjects to take, pending projects, and academic research to do. As part of that research, collecting information and data is essential for a final delivery that will give you an A and a 100% reliable result. Researchers use academic papers to earn colleagues’ respect, be a pioneer in their respective fields, and participate in future related research.

But how do you create an excellent quality academic research paper? Why is data collection a crucial point to it? In the following blog post, you’ll find the answers to those questions.

What is academic research?

Academic research is the best tool universities have to create or enhance knowledge and facilitate learning. Additionally, most academic research helps solve different social and economic problems in the community surrounding the university where it originated.

Academic research is a systematic process of studying a research problem or situation, where the intention is to identify facts that help solve the problem or deal with the situation.

Academic research aims to generate new knowledge that improves social development. This research is one of the essential responsibilities of a faculty member working at an educational institution. 

Mainly this focuses on scientific discoveries, conducting studies into various aspects of life, with the eventual aim of developing a more in-depth knowledge of the subject.

It’s all about using new research techniques, creating studies into untouched areas of life, and giving us a better understanding of the world in which we live. There are four types of Academic Research:

• Exploratory research to identify new situations/problems. Because of its nature, this type of research is often qualitative; however, a study with a large sample in an exploratory manner can incorporate qualitative research.
• Descriptive research identifies the characteristics of a particular phenomenon without investigating its causes.
• Explanatory research identifies cause-and-effect relationships in a problem, allowing generalizations that can apply in similar situations.
• Correlational research identifies the relationship between two or more variables and the effect on the system when a change in one of them occurs.

Characteristics of academic research

Academic research is more than just choosing a topic, collecting data, and putting it together on paper. To be considered good research, this must meet specific criteria to ensure the quality of the research. Some of the characteristics of good research are:

• Good research anchors to its topic question; this is the critical factor in the research. When coming up with the research question, try using FINER criteria (Feasible, Interesting, Novelty, Ethical, and Relevant)
• Every research follows a systematic and appropriate methodology.
• Acknowledgment of previous research is critical for the discovery of new knowledge. Using articles, journals, and investigations done in the past will give you a notion of the study’s direction.
• The criteria of good research is that it is representative and generalizable; this refers to the sample’s ability to represent a larger group with minimal variation.
• External validation of the research is a huge differentiator, as it gives recognition to the investigation for it to be used in future studies. 

Objectives of academic research

Academic research seeks to advance new knowledge and has relevance based on solving problems that contribute to the improvement of society.

When you perform academic research, you are essentially trying to solve a mystery—you want to know how something works or why something happened. In other words, you want to answer a question that you, academics, and professionals have about the world. This is one of the most fundamental reasons for performing research.

The process doesn’t stop right after solving the problem. Academic research needs to be presented, the most common way is through an academic paper, but if the paper is outstanding in quality, it can be published in professional journals.

Importance of academic research

As we have said before, academic research facilitates learning, highlights key issues in society, and can promote the growth of students.

• Facilitates the learning process: It is the best activity to develop or improve knowledge and allows to understand specific problems through varied angles that were never identified or talked about much. While conducting the study, you collect the evidence based on facts and rationale. This is how academic research papers open the doors for more discourse and debate.
• Highlights the problems: Generally, academic research highlights some problems that prevail in society, which could be related to cultural norms, health, education, specific practices, etc.
• Leads to the personal growth of students: This process helps in the development of skills. Students learn to identify a problem and arrive at a possible solution or develop a point of view on a specific issue. In addition, they develop skills such as big data analysis, critical thinking, time management, and organization.

Difference between academic and professional research  

There are several types of research, depending on the perspective and objective of each one. If we talk about academic research, it mainly focuses on making new discoveries for the scientific community.

Instead, professional research is more geared towards solving a specific problem for an organization, often a company or its clients. It could be called the next step of the investigation because it is at the same time collecting information and finding a solution, only applied to different approaches and objectives of life, one academic and one more from working life.

Academic research focuses on the research objectives and questions that arise from independent researchers. It uses formal, scientific, and systematic procedures to discover answers and to prove or reject existing theories.

On the other hand, professional research is defined as work carried out to achieve the objectives of an organization and focuses on the research objectives that arise from the requirements of the company. 

You may or may not use formal, scientific, and systematic procedures to discover answers. It is not based on theory and may not require a representative sample.

LEARN MORE: Descriptive Research vs. Correlational Research

Academic research methods

Research methods are the strategies, processes, or techniques used to collect data or analyze evidence to uncover new information or better understand a topic.

Different types of research methods use different tools for data collection. The principal tools for this type of research are interviews, focus groups, observation, and surveys.

• Interviews . A qualitative interview is the best research technique that allows the researcher to gather data from the subject using open-ended questions. The most important aspect of an interview is how it is made. Typically, it would be a one-on-one conversation focusing on the substance of the question.
• Focus group. Focus group is one of the best examples of qualitative data in education or in academic research. It is also a qualitative approach to gathering information. The main difference from an interview is that the group is composed of 6 – 10 people purposely selected to understand the perception of a social group. Rather than trying to understand a more significant population in the form of statistics, the focus group is directed by a moderator to keep the group in topic conversation. Hence, all the participants contribute to the research.
• Observation. Observation is a method of data collection that incorporates the researcher into the natural setting where the participants or the phenomenon is happening. This enables the researcher to see what is happening in real-time, eliminating some bias that interviews or focus groups can have by having the moderator intervene with the subjects.
• Surveys . A survey is a research method used to collect data from a determined population to gain information on a subject of interest. The nature of the survey allows for gathering the information at any given time and typically takes no time, depending on the research. Another benefit of a survey is its quantitative approach, which makes it easier to present it comprehensively.

Tips for doing academic research through surveys

Data collection is the process by which information is collected and measured based on our interests, taking the right path to answer specific research questions , test our hypotheses and predict the results.

The data collected should be similar to that of the study area, while the methods vary depending on the rules and regulations of each industry. Emphasis must be placed on ensuring accuracy and honesty in data collection, this is very important. 

Regardless of what qualitative and quantitative research methods you are conducting in your academic research, data collection must be accurate, which is essential to maintaining the integrity of academic research.

01. Perform effective sampling

Survey sampling size has to do with correctly defining the number of participants. This is one of the main steps in designing and organizing a survey. 

• Main concept : Before starting your academic research survey, you must confirm the study population and give it the correct follow-up. We must be aware that a change throughout the research process can critically affect the reality of the data collected. 
• Diversity: Ensuring the diversity of your sample and getting them to participate can be tedious work. However, it is very important to have a representative sample of the population to obtain richness in the responses. 
• Clarity : There are several limitations to determining the size and structure of the population sample. It is crucial that researchers describe their limitations and maintain the procedures they follow to select the sample transparently so that the results of surveys are seen from the correct perspective.

02. Select Survey Software 

We at QuestionPro are interested in fostering an interest in students in conducting effective academic research. It is because of students that we have different tools that will help them achieve it effectively:

The platform allows you to select different types of questions such as multiple choice , open , matrix type , satisfaction questions with smileys , and many more. 

In addition, our survey software allows students to email their survey, share it on social media, send it via SMS, etc., to facilitate data collection. 

03. Analysis of the responses

Analyzing the responses will help to know in detail the data obtained in the data collection process and confirm or refute the established hypothesis. 

With QuestionPro, it is possible to view survey data responses in real time. This way, you can effectively perform in-depth analysis for your academic research.

We have for you this article on data analysis, techniques, and step-by-step guide .

04. Research report

There are several essential points to consider when reporting the research results. All reports should be educational, relevant to the target audience, and customized to each company’s needs. 

The report of your academic research can be presented through visual presentations, written on an academic paper, or electronic reports. The way you present your survey results will make a big difference. A complete, formal report usually includes the following elements:

• Cover Sheet
• Introduction
• Research Purpose
• Survey Sample
• Methodology
• Conclusion and Recommendations
• Contact Information

QuestionPro platform also provides you with survey dashboards that will be very useful for presenting a report of results.

Online surveys will help you obtain the data you need for decision-making in your academic research. However, it is important that before collecting a series of data, you choose the right topic, the right questions to ask, and the type of survey you will carry out. 

The design of your survey and the target audience, that is, the right people to answer the questions in your questionnaire, will depend on all of the above.

We know that surveys play an important role in educational projects. That is why our platform allows you to conduct quantitative and qualitative research, polls, questionnaires, and online surveys. 

QuestionPro is a global company concerned about education. That’s why we offer academic alliances so that university students and teachers obtain our tool to take online exams, create forms, conduct research projects, and perform data analysis.

If you are interested in using our platform to carry out academic research, we invite you to learn about the benefits of our academic alliances. Take advantage of everything you can achieve by implementing our tool into your education services and start carrying powerful research to your education institution.

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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.

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• Research is an Activity and a Subject of Study: A Proposed Metaconcept and Its Practical Application (76798 views)
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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.

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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.

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

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ISSN: 2150-6701

The advent of the AUKUS partnership heralds a transformative era in Australia’s strategic posture and scientific landscape, propelling us into the vanguard of cutting-edge research and development. However, this newfound prominence also exposes a critical vulnerability: the susceptibility of our academic institutions to foreign espionage and intellectual property theft, a menace that threatens to undermine our economic prosperity and strategic autonomy.

Australia’s universities, well respected for their open research environment and spirit of international collaboration, are now facing an insidious threat. Their very strengths—free exchange of ideas, cross-pollination of diverse perspectives, collaborative spirit that drives innovation—are being exploited by foreign actors seeking to pilfer our intellectual capital and erode our competitive edge. This threat, once relegated to the realm of Cold War espionage thrillers, has become a stark reality in the 21st century, with the Chinese Communist Party emerging as a principal antagonist.

China’s relentless pursuit of technological dominance has manifested in a multifaceted campaign of intellectual property theft, cyber espionage and talent recruitment. The CCP has openly declared its ambition to become a global leader in science and technology by 2050, and it is willing to use any means to achieve this goal. The Thousand Talents Program, a state-sponsored initiative aimed at luring overseas scientists to China, offers a stark example. By incentivising the transfer of knowledge and expertise to China, often in violation of intellectual property agreements or export controls, the CCP seeks to leapfrog decades of research and development, gaining a strategic advantage at our expense.

Australia’s universities, with their extensive international partnerships and research collaborations, are particularly vulnerable to this threat. Recent events underscore the urgency of the situation. In 2019, the University of Technology Sydney found itself in the middle of a national controversy when it was revealed that its Centre for Quantum Software and Information had received $10 million in funding from a Chinese company with close ties to the People’s Liberation Army. This incident exposed the potential for cutting-edge quantum research, with far-reaching implications for cryptography and national security, to be diverted for military purposes.

In 2020, the University of Queensland faced intense scrutiny for its partnership with Huawei, a Chinese telecommunications giant. This collaboration, which involved joint research projects and the establishment of a Huawei-funded research centre at the university, raised alarms about the company’s access to sensitive research data and intellectual property, potentially compromising Australia’s telecommunications infrastructure and national security.

The Australian National University is not immune. In 2021, it was crippled by a sophisticated cyberattack that compromised the personal information of thousands of students and staff. While the perpetrators were never definitively identified, cybersecurity experts widely suspected the involvement of Chinese state-sponsored hackers seeking to infiltrate Australia’s research networks and steal sensitive data.

The Australian Security Intelligence Organisation (ASIO) has repeatedly warned about the threat of foreign interference in Australian universities, particularly from China. In 2020, ASIO Director-General Mike Burgess said the agency was investigating ‘hundreds’ of cases of foreign interference in Australia’s research sector. He warned that foreign governments were targeting universities to steal sensitive research, influence academic discourse and recruit agents.

The AUKUS partnership, while offering immense opportunities for collaboration and technological advancement, also amplifies the risks we face. As Australia engages in joint research and development projects with allies, we must be vigilant in safeguarding our intellectual property and ensuring that our collaborative efforts do not inadvertently benefit our adversaries.

The imperative to protect our research secrets is not unique to Australia. Western democracies are grappling with similar challenges. The United States, through its Committee on Foreign Investment in the United States (CFIUS), has long exercised its power to scrutinise and block foreign investments in sensitive technology sectors. In recent years, the US has also intensified its efforts to counter Chinese economic espionage and trade secret theft through law enforcement actions and diplomatic pressure.

Britain, recognising the growing threat to its research and innovation ecosystem, introduced its National Security and Investment Act in 2021. This legislation grants the government sweeping powers to scrutinise and potentially block foreign investments in critical sectors, including research and development.

To safeguard its research crown jewels, Australia must adopt a multi-pronged approach that includes:

Robust vetting:  implementing a rigorous vetting process for researchers in sensitive fields, scrutinising their backgrounds, affiliations and funding sources.

Transparency and disclosure:  mandating clear disclosure of all foreign funding and collaborations in research projects.

Security awareness training:  educating researchers and administrators about the risks of foreign interference and the importance of safeguarding sensitive information.

Cybersecurity reinforcement:  investing in robust cybersecurity infrastructure and protocols to protect against cyberattacks and data breaches.

Collaboration and information Sharing:  fostering closer cooperation between universities, government agencies and intelligence services to identify and counter threats in real time.

Export controls:  strengthening export control mechanisms to prevent the unauthorized transfer of sensitive technologies and research data.

Legislative framework:  updating and enforcing laws that address foreign interference in academic institutions and research activities.

These measures, while not a panacea, would be a crucial step towards protecting Australia’s research secrets from the clutches of those who seek to exploit them for their own gain. The AUKUS partnership provides a unique opportunity for Australia to enhance our technological prowess and national security. By embracing a proactive and vigilant approach to research security, we can ensure that this partnership benefits our nation, not our adversaries.

Andrew Horton is the chief operating officer of ASPI. Image of the University of Queensland: Universities Australia .

• intellectual property
• universities

The Strategist — The Australian Strategic Policy Institute Blog. Copyright © 2024

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Article Contents

“ keep it a secret ”: leaked documents suggest philip morris international, and its japanese affiliate, continue to exploit science for profit.

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Sophie Braznell, Louis Laurence, Iona Fitzpatrick, Anna B Gilmore, “ Keep it a secret ”: leaked documents suggest Philip Morris International, and its Japanese affiliate, continue to exploit science for profit, Nicotine & Tobacco Research , 2024;, ntae101, https://doi.org/10.1093/ntr/ntae101

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The tobacco industry has a long history of manipulating science to conceal the harms of its products. As part of its proclaimed transformation, the world’s largest tobacco company, Philip Morris International (PMI), states it conducts “ transparent science ”. This paper uses recently leaked documents from PMI and its Japanese affiliate, Philip Morris Japan (PMJ), to examine its contemporary scientific practices.

23 documents dating 2012 through 2020 available from Truth Tobacco Industry Documents Library were examined using Forster's hermeneutic approach to analysing corporate documentation. Thematic analysis using the Science for Profit Model was conducted to assess whether PMI/PMJ employed known corporate strategies to influence science in their interests.

PMJ contracted a third-party external research organisation, CMIC, to covertly fund a study on smoking cessation conducted by Kyoto University academics. No public record of PMJ’s funding or involvement in this study was found. PMJ paid life sciences consultancy, FTI-Innovations, ¥3,000,000 (approx. £20,000) a month between 2014 and 2019 to undertake extensive science-adjacent work, including building relationships with key scientific opinion leaders and using academic events to promote PMI’s science, products and messaging. FTI-Innovation’s work was hidden internally and externally. These activities resemble known strategies to influence the conduct, publication and reach of science, and conceal scientific activities.

The documents reveal PMI/PMJ’s recent activities mirror past practices to manipulate science, undermining PMI’s proclaimed transformation. Tobacco industry scientific practices remain a threat to public health, highlighting the urgent need for reform to protect science from the tobacco industry’s vested interests.

Implications: Japan is a key market for PMI, being a launch market for IQOS and having the highest heated tobacco product use globally. Our findings, in conjunction with other recent evidence, challenge PMI’s assertion that it is a source of credible science and cast doubt on the quality and ethical defensibility of its research, especially its studies conducted in Japan. This, in turn, brings into question the true public health impacts of its products. There is urgent need to reform the way tobacco-related science is funded and conducted. Implementation of models through which research can be funded using the industry’s profits while minimising its influence should be explored.

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Research work, personal belongings: Students, faculty at IIT Delhi count losses after lab flooded

Amid ongoing cleaning at the lab, several students are compelled to pause their research work till it opens again. Many faculty members too have suffered losses.

Years of research work, including chemical samples, personal property, and costly equipment – students and faculty at the Indian Institute of Technology Delhi’s Kusuma School of Biological Sciences (KSBS) are grappling with heavy losses after last week’s torrential rain in the city.

It was around 10.30 am on June 28 when a PhD scholar found out that the lab in the lower basement of the KSBS was flooded amid a massive spell of rain. “When I went into the lab to collect my stuff, the water was almost up to the knee level. I have lost my laptop. I am still trying to dry and recover five years of research work,” the student, in his final year, shared.

“Waterlogging in the lab happens almost every year due to rain but this time it has caused a lot of damage to the equipment and research work of several students and teachers. All we are asking for is good working conditions in India’s premier Institute,” he added.

Another PhD student, who is also in his final year, expressed concern about the safety of sanitation workers who are deployed to clean the work. “The water that is floating around in the basement is filled with hazardous chemicals but we see sanitation workers cleaning the lab without any special safety gear. We dispose of such material carefully even while performing experiments,” the scholar said.

“The sanitation workers have not been given any personal protective equipment. So we’re really worried about them,” said a third-year student. Claiming that the water in the lab was dumped without treatment, he added, “All of this is highly unethical, and may lead to further damage to the ecosystem or the populace at large.”

A 10th-semester student lost the chemicals, which are needed while performing experiments. “It might take some time to regenerate them and test them again if they are effective in experiments,” he said.

A student who lost research work of at least one year said, “Every year the water rises to one foot… this year it was five to six feet. I lost all the reagents required for my research.”

A PhD scholar said, “We do not have any place to work for the coming three to four months. Even if we shift to new buildings, there is no equipment as the damaged ones would take much time to repair. It Will take at least one year to start everything.”

The Indian Express reached out to IIT Delhi director Rangan Banerjee, and Head of Department at KSBS Biswajit Kundu but received no response in this regard. Press relations officer Shiv Yadav declined to comment on the issue.

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Cristiano Ronaldo's last chance to win the European Championship title has come to an end as France defeated Portugal in a dramatic penalty shootout. The 39-year-old had announced that this would be his final Euros tournament, but despite his efforts, he could not lead his team to victory. France will now face Spain in the semi-final.

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