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Generative AI in Research

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. For a review of generative AI basics, visit our AI 101: Starter Guide.

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have been developed and trained using vast compilations of text, image, audio or video data from both published and unpublished works.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. 

Generative AI at UNLV

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

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.

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.

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.

Vendors and Contractors

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.

Generative AI Product Tracker

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. Explore the following to identify tools that may be applicable to your research and work:

  • Future Tools' List
  • Generative AI Product Tracker (regularly updated by Ithaka S+R's)
    • From the institution: "The Generative AI Product Tracker lists generative AI products that are either marketed specifically towards postsecondary faculty or students or appear to be actively in use by postsecondary faculty or students for teaching, learning, or research activities. The Tracker is a living document, which we update regularly as new products enter the market or new information about existing products becomes available. For more information, see our issue brief, Generative AI in Higher Ed: The Product Landscape. Thanks to Gary Price of Library Journal’s infoDOCKET for invaluable help keeping track of new product releases."

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

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