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Use of Generative AI in Research: Overview

This guide will help you to navigate some of the tools and information needed to consider using generative artificial intelligence (AI) in your research

What is Generative AI?

This guide is designed to provide an overview of the considerations a researcher should review when using these tools so they can effectively incorporate them into their work and evaluate research produced with the assistance of generative AI. 

Generative AI tools are software that use large datasets to predict best outputs in response to text-based prompts. These tools are able to respond to, and with, natural language in a sophisticated manner that mimics conversation with another human, or produce images, audio, or video that may appear human-created to an untrained eye (or ear).

These Generative AI tools have been developed and trained using vast compilations of text, image, audio or video data from both published and unpublished works. Some can mimic the style of well known genres, authors and artists. Often, persons are employed to train these software tools and interacting with them may continue the tool's training and fine-tuning to achieve a more-desirable output. Because the definition of "best output" is relative, and the data sources used to train each model may not be disclosed to you, factual or ethical outputs as you might define them are not guaranteed. 


For information on ChatGPT specifically, see ChatGPT: Helpful Information and Resources for Instructors

UNLV Policies on Using AI in Research

Currently UNLV does not have a policy on the use of generative AI tools for research, but it does provide guidance for instructors.

Questions about whether your proposed use complies with applicable legislation, policies, procedures, and guidelines should be discussed with the Office of Research Integrity to ensure that generative AI is being utilized in responsible and ethical ways.

Using Generative AI in your Research

Generative AI offers vast potential to accelerate research and dissemination. Researchers are applying these tools in various ways including in critical analysis, data and code generation, data and text analysis, synthesis, design, and written content generation. Generative AI can also assist in creating research ideas and proposals, fostering creativity and innovation. While generative AI has the potential to streamline many aspects of academic research, researchers should be aware of its limitations. 

Transparency of use

When using generative AI tools in your research you should articulate how it has been used. Share with co-investigators, and collaborators your planned use of generative AI tools and familiarize yourself with scholarly publishers’ policies on the use and acknowledgement of generative AI. Example policies are linked below, and publishers will update and evolve these over time in response to the changing  landscape. Publishers are increasingly refusing to accept generative AI as a contributing/co-author but support transparency of use through inclusion in methods or other appropriate sections of a manuscript.


When working with vendors or subcontractors, inquire about their practices of using generative AI. Additional terms and conditions may need to be included in any resulting agreement to ensure responsible and ethical use of these tools by collaborating organizations.

Selecting a Tool

UNLV faculty, staff, and students should consider utilizing tools and services that exhibit the National Institute of Standards and Technology’s (NIST’s) characteristics of trustworthy AI. Consult this list of generative AI tools to identify tools that may be applicable to your research.

Note: This list is not created by UNLV Libraries and inclusion of any tool on this list is not an endorsement of the suitability of a particular generative AI system for use in research at UNLV.

Integration of Generative AI in other tools

AI is not a new technology. In many applications, AI is already in use or is part of what powers services that researchers use every day.

In the context of the University Libraries, for example, AI may be behind indexing of research databases, recommender systems in software, chatbots at service points, or in the summarizing of academic publications. The efficiency created by AI to create automation can offer some attractive solutions to tedious problems, such as manual transcription of voice recordings to create more accessible captions, automating the design of custom images for marketing materials and presentations, translation for presenting content in multiple languages, and the generation of computer code.

As information in our world is highly digital, researchers should expect that AI is being used to save time and money. It is too late to reject AI, so it is preferable to remain curious and investigate ways it may be integrated into tools that you use in your work.

Contributions to this Guide

Thanks to the following UNLV Libraries staff and librarians for making the information in this guide possible:

Annette Day, Kevin Sebastian, Sue Wainscott, Christina Miskey, Cory Lampert, Chelsea Heinbach, Andrea Wirth

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