IDEAS home Printed from https://ideas.repec.org/a/bla/jinfst/v76y2025i6p867-883.html
   My bibliography  Save this article

College students' credibility assessments of GenAI‐generated information for academic tasks: An interview study

Author

Listed:
  • Wonchan Choi
  • Hyerin Bak
  • Jiaxin An
  • Yan Zhang
  • Besiki Stvilia

Abstract

The study explored college students' use of generative artificial intelligence (GenAI) tools, such as ChatGPT, for academic tasks and their perceptions and behaviors in assessing the credibility of GenAI‐generated information. Semistructured interviews were conducted with 25 college students in the United States. Interview transcripts were analyzed using the qualitative content analysis method. The study identified various types of academic tasks for which students used ChatGPT, including writing, programming, and learning. Guided by two models of credibility assessment Hilligoss and Rieh (2008); Metzger (2007), six factors influencing students' motivation and ability to assess the credibility of GenAI‐generated information were identified (e.g., task salience, social pressure). We also identified 9 constructs (e.g., refinedness, explainability), 5 heuristics (e.g., inter‐ and intrasystem consistency heuristics), and 10 cues (e.g., version and tone) used by students to assess the credibility of GenAI‐generated information. This study provides theoretical and empirical findings regarding students' use of GenAI tools in the academic context and credibility evaluation of the system outputs using rich, qualitative interview data.

Suggested Citation

  • Wonchan Choi & Hyerin Bak & Jiaxin An & Yan Zhang & Besiki Stvilia, 2025. "College students' credibility assessments of GenAI‐generated information for academic tasks: An interview study," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 76(6), pages 867-883, June.
  • Handle: RePEc:bla:jinfst:v:76:y:2025:i:6:p:867-883
    DOI: 10.1002/asi.24978
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asi.24978
    Download Restriction: no

    File URL: https://libkey.io/10.1002/asi.24978?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
    ---><---

    More about this item

    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:bla:jinfst:v:76:y:2025:i:6:p:867-883. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.asis.org .

    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.