IDEAS home Printed from https://ideas.repec.org/a/jfr/wjel11/v14y2024i2p502.html
   My bibliography  Save this article

Evaluating AI-Generated Emails: A Comparative Efficiency Analysis

Author

Listed:
  • Marina Jovic
  • Salaheddine Mnasri

Abstract

This study investigates the efficiency of large language models (LLMs) in producing routine, negative, and persuasive business emails for educational purposes within the context of Business Writing. Specifically, it compares the outputs generated by four widely-used LLMs (ChatGPT 3.5, Llama 2, Bing Chat, and Bard) when presented with identical email scenarios. These generated emails are evaluated using an elaborate rubric, allowing for a systematic assessment of LLMs' performance across three distinct email types. The results of the study show that the output with the same prompt varies greatly despite the rather formulaic nature of business emails. For instance, some LLMs struggle with following the requested structure and maintaining consistency in tone, while others have issues with unity and conciseness. The findings of this research hold implications for teaching business writing (rubrics, task instructions, in-class implementation), as well as for the integration of AI in professional communication at large.

Suggested Citation

  • Marina Jovic & Salaheddine Mnasri, 2024. "Evaluating AI-Generated Emails: A Comparative Efficiency Analysis," World Journal of English Language, Sciedu Press, vol. 14(2), pages 502-502, March.
  • Handle: RePEc:jfr:wjel11:v:14:y:2024:i:2:p:502
    as

    Download full text from publisher

    File URL: https://www.sciedupress.com/journal/index.php/wjel/article/download/24659/15730
    Download Restriction: no

    File URL: https://www.sciedupress.com/journal/index.php/wjel/article/view/24659
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:igg:jssoe0:v:8:y:2018:i:3:p:1-17 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ketmanto Wangsa & Shakir Karim & Ergun Gide & Mahmoud Elkhodr, 2024. "A Systematic Review and Comprehensive Analysis of Pioneering AI Chatbot Models from Education to Healthcare: ChatGPT, Bard, Llama, Ernie and Grok," Future Internet, MDPI, vol. 16(7), pages 1-23, June.

    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:jfr:wjel11:v:14:y:2024:i:2:p:502. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sciedu Press (email available below). General contact details of provider: http://wjel.sciedupress.com .

    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.