IDEAS home Printed from https://ideas.repec.org/a/eee/jeborg/v246y2026ics0167268126001502.html

12 best practices for leveraging Generative AI in experimental research

Author

Listed:
  • Chang, Samuel
  • Kennedy, Andrew
  • Leonard, Aaron
  • List, John A.

Abstract

We provide twelve best practices and discuss how each practice can help researchers accurately, credibly, and ethically use Generative AI (GenAI) to enhance the four stages of experimental research. First, in the pre-treatment stage, GenAI can aid in pre-registration procedures, data privacy concerns, and ethical considerations specific to GenAI usage. Second, in the design and implementation stage, we focus on GenAI’s role in identifying new channels of variation, piloting and documentation, and upholding the four exclusion restrictions. Third, the analysis stage outlines how prompting and training set bias can impact results as well as necessary steps to ensure replicability. We end by discussing forward-looking best practices that are likely to gain importance as GenAI evolves.

Suggested Citation

  • Chang, Samuel & Kennedy, Andrew & Leonard, Aaron & List, John A., 2026. "12 best practices for leveraging Generative AI in experimental research," Journal of Economic Behavior & Organization, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:jeborg:v:246:y:2026:i:c:s0167268126001502
    DOI: 10.1016/j.jebo.2026.107564
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167268126001502
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jebo.2026.107564?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • B40 - Schools of Economic Thought and Methodology - - Economic Methodology - - - General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jeborg:v:246:y:2026:i:c:s0167268126001502. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jebo .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.