IDEAS home Printed from https://ideas.repec.org/h/spr/lnichp/978-3-032-00815-2_5.html

Do We Perceive Virtual Teachers as Human? An EEG Experiment Proposal for Investigating the Cognition of AI Teaching Expectations

In: Information Systems and Neuroscience

Author

Listed:
  • Colin Conrad

    (Dalhousie University)

  • Nana Boakye

    (Dalhousie University)

  • Aaron J. Newman

    (Dalhousie University)

Abstract

Advances in generative artificial intelligence (AI) have made virtual agents ubiquitous, leading to widespread disruption of higher education. Many are asking whether these agents will replace educators altogether. In this paper, we explore some of the literature on virtual agents and past work from the educational technology literature to outline one of the key limitations of educational virtual agents: their inability to generate social presence. We then provide reasons why the N400 event-related potential (ERP) may be sensitive to aspects of social presence and thus reflect associated perceptions of virtual teachers and virtual agents broadly. We conclude with a proposal for an experiment which could establish cognitive differences, as measured by N400 amplitude differences and their relationship to Cloze probability of phrase endings. This would also suggest that user expectations are important considerations in the effective design of virtual teachers.

Suggested Citation

  • Colin Conrad & Nana Boakye & Aaron J. Newman, 2025. "Do We Perceive Virtual Teachers as Human? An EEG Experiment Proposal for Investigating the Cognition of AI Teaching Expectations," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph & Gernot (ed.), Information Systems and Neuroscience, pages 57-64, Springer.
  • Handle: RePEc:spr:lnichp:978-3-032-00815-2_5
    DOI: 10.1007/978-3-032-00815-2_5
    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

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    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:spr:lnichp:978-3-032-00815-2_5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.