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Generative Artificial Intelligence (Gen AI)

Common Gen AI Research Use Cases

Generative AI tools can be used in a variety of phases of the research lifecycle, including, but not limited to the following:

Phase

Potential Uses

Notes
 Ideation

"AI can automatically generate research questions based on a given dataset or topic that can serve as starting points for researchers to refine and develop into hypotheses.

Recommended Resources:

"Generate topics for your research paper with ChatGPT," by University of Arizona Libraries

"Writing Effective Text Prompts," by Sheridan College Library and Learning Services (includes steps for brainstorming and narrowing topics, writing research questions, and identifying keywords for your search)
Literature

"AI can accelerate the literature review process by analyzing and summarizing a body of literature on a topic, identifying relevant trends, patterns, and gaps in existing knowledge. (e.g., Elicit, Consensus, ResearchRabbit)"

Be aware that these tools will have less access to materials than what you are able to access via the Libraries, may not be clear about the underlying scholarly sources being used, and will necessitate additional quality checking of results (learn more about AI hallucinations). Thus, we recommend 1) starting with library resources and THEN moving on to any additional tools of interest to see if they help surface results that may not have appeared in your original searches, and 2) carefully verifying your results ("What should I do if I can't find the citations ChatGPT gave me?").
Design

"AI algorithms can assist researchers in designing experiments by suggesting variables, methodologies, and potential outcomes based on existing data."

 
Analysis "AI can aid in processing and analyzing large datasets, making it easier to identify emerging trends, correlations, outliers, and other patterns."

Recommended Online Resources Available via the Libraries:

Törnberg, P., (2024). How to Use Large-Language Models for Text Analysis [How-to Guide]. Sage Research Methods: Doing Research Online. https://doi.org/10.4135/9781529683707

Munk, A. K., (2022). How to Use Computer Vision to Study Large Corpora of Images [How-to Guide]. Sage Research Methods: Doing Research Online. https://doi.org/10.4135/9781529611465
Writing "AI can assist in drafting, proof-reading, and editing research papers."  
Sharing "AI-driven recommendation systems can help researchers connect with peers, collaborators, and experts in their field, fostering interdisciplinary collaboration and knowledge sharing."  

Key Considerations for Using Gen AI in Research

1) Ensure it is ethical and permissible for you to use generative AI output in your context.

  • While some products and tools are open source others are commercial and commonly capture information about users, biases in these products are derived from the data sets used to train them. Generative AI tools commonly reproduce biases inherently found in the data sets they are trained on which can perpetuate both harm and misinformation.
     
  • Be mindful of what you input into tools: Avoid putting private/confidential information or significant portions of intellectual property you do not have the rights or permissions to into these systems. All content entered may become part of the tool’s dataset and may inadvertently resurface in response to other prompts.
     
  • To confirm permissibility and parameters for working with generative AI tools on assignments, consult the campus honor code, syllabus/assignment instructions, and/or check with your instructor; check publisher websites for any guidelines provided about generative AI use and acknowledgement.  
     

2) Consult multiple sources of information to fact-check/verify the generative AI output you plan to use.

  • Generative AI tools are not the same as research databases–In some cases these products will have been trained using data from scholarly resources, but unlike a discovery platform which will point you directly to the source literature, generative AI tools are designed to 'generate' their own content or output.
     
  • As different generative AI products are trained on different large language models, the data ranges and accuracy of the included information will vary from tool to tool. When searching for timely or current information, an AI tool will only be as good as the dates of coverage included in the data set it was trained on.
     

3) Cite/acknowledge your use of generative AI output as appropriate in your context. 

  • Be explicit in indicating how you have used these tools in the creation of your work: 
     
    • Keep a record of prompts and any intellectual property (IP) you have used in the creation of output.
       
    • Review attribution guidelines according to the style (e.g., APA, MLA) you are using.  
       
    • If you are publishing your work, review any requirements or policies that address the use of generative AI tools in your research. These policies will indicate whether generative AI can be used and how the use of these tools should be disclosed.
       
  • Some copyright considerations for use of generative AI output in various research contexts include the following:
     
    • Be mindful of what you input into tools: Avoid putting confidential information or significant portions of intellectual property you do not have the rights or permissions to into these systems. All content entered may become part of the tool’s dataset and may inadvertently resurface in response to other prompts. 
       
    • Review the terms of service of each tool: These terms will dictate use and ownership of input/output and they are subject to change without notice.