IDEAS home Printed from https://ideas.repec.org/a/rfa/smcjnl/v14y2026i2p150-160.html

Generative AI and Media Convergence in Education: Transforming Student Communication on Social Media

Author

Listed:
  • Wei Yan Lyu

Abstract

Generative Artificial Intelligence (AI) and media convergence are prompting a rethinking of how students communicate, allowing for visually active creation and interpretation in educational and social media contexts. Although there has been descriptive analysis of media literacy training, there is a limited empirical literature on the training. This research examines the impact of media literacy training on enhancing students' ability to verify AI-generated images in educational and digital communication contexts. A quasi-experimental design was adopted with a purposive sample of 430 undergraduate students representing both genders. The training program was designed to strengthen four key media literacy competencies- access, analysis, collective reasoning, and evaluation. The experiment was implemented using SPSS to assess the program's effectiveness. Pre-test and post-test scores were analyzed using paired sample t-test, correlation analysis, and regression analysis. The analysis revealed statistically significant differences (p

Suggested Citation

  • Wei Yan Lyu, 2026. "Generative AI and Media Convergence in Education: Transforming Student Communication on Social Media," Studies in Media and Communication, Redfame publishing, vol. 14(2), pages 150-160, June.
    • Handle: RePEc:rfa:smcjnl:v:14:y:2026:i:2:p:150-160
    as

    Download full text from publisher

    File URL: https://redfame.com/journal/index.php/smc/article/download/8363/7159
    Download Restriction: no
    File URL: https://redfame.com/journal/index.php/smc/article/view/8363
    Download Restriction: no
    ---><---

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - 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:rfa:smcjnl:v:14:y:2026:i:2:p:150-160. 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: Redfame publishing (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.