IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-05610520.html

agentic ai in marketing education: toward autonomous workflow orchestration

Author

Listed:
  • Aria Teimourzadeh

    (UWO - Western University [Ontario], CRIISEA - Centre de Recherche sur les Institutions, l'Industrie et les Systèmes Économiques d'Amiens - UR UPJV 3908 - UPJV - Université de Picardie Jules Verne)

  • Samantha Kakavand

    (CRIISEA - Centre de Recherche sur les Institutions, l'Industrie et les Systèmes Économiques d'Amiens - UR UPJV 3908 - UPJV - Université de Picardie Jules Verne, CERM ESC Amiens)

  • Benjamin Kakavand

    (CRIISEA - Centre de Recherche sur les Institutions, l'Industrie et les Systèmes Économiques d'Amiens - UR UPJV 3908 - UPJV - Université de Picardie Jules Verne, School of Business [Kitchener, Ont.] - Conestoga College [Kitchener, Ont.])

Abstract

Artificial intelligence (AI) is reshaping marketing practice and consequently marketing education. While prior pedagogical research has examined traditional AI tasks and more recently generative AI (GenAI) in marketing education, limited attention has been devoted to the emerging paradigm of agentic AI. This requires instructors and students to move from content creation toward system design, process orchestration and execution of marketing tasks using autonomous AI agents. This paper introduces a novel workflow automation assignment that integrates agentic AI into marketing education. We conceptualize agentic AI for marketing education, distinguish it from traditional and generative AI and develop hypotheses regarding its impact on student satisfaction, perceived learning, and engagement in marketing process automation. In this research, a survey was conducted to measure the teaching effectiveness and overall satisfaction of graduate level marketing students (n = 71) in a French university. The evidence suggests that the proposed assignment using n8n platform has positive outcomes in students' learning experience and engagement.

Suggested Citation

  • Aria Teimourzadeh & Samantha Kakavand & Benjamin Kakavand, 2026. "agentic ai in marketing education: toward autonomous workflow orchestration," Post-Print hal-05610520, HAL.
  • Handle: RePEc:hal:journl:hal-05610520
    DOI: 10.1080/10528008.2026.2659869
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:hal:journl:hal-05610520. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.