IDEAS home Printed from https://ideas.repec.org/a/igg/jopcd0/v16y2026i1p1-25.html

Beyond Intelligence Quotient in the Artificial Intelligence Era: Psychometric Validation and Perceived Boundaries

Author

Listed:
  • Tzu-chin Rejoice Chou

    (Ming Chuan University, Taiwan)

Abstract

As generative artificial intelligence (AI) is increasingly incorporated into higher education, renewed attention is required to understand how human capabilities are conceptualized relative to AI. This study has two aims. First, it examines the psychometric properties of the second edition of the Eight Multiple Intelligences Questionnaire among Taiwanese university students. Second, the study explores students' perceptions of human-AI strengths across intelligence domains. Data were collected from 936 students at a private university in Taiwan. Exploratory factor analysis supported an eight-factor structure, explaining 80.34% of the total variance. Reliability was satisfactory, with Cronbach's α and composite reliability greater than .90. Students perceived AI as stronger in linguistic, logical-mathematical, and spatial domains, while humans were viewed as stronger in interpersonal, intrapersonal, bodily-kinesthetic, naturalistic, and existential domains. These findings support the instrument while clarifying human-AI capability boundaries. The results offer implications for designing AI-assisted, human-led courses and online pedagogy.

Suggested Citation

  • Tzu-chin Rejoice Chou, 2026. "Beyond Intelligence Quotient in the Artificial Intelligence Era: Psychometric Validation and Perceived Boundaries," International Journal of Online Pedagogy and Course Design (IJOPCD), IGI Global Scientific Publishing, vol. 16(1), pages 1-25, January.
    • Handle: RePEc:igg:jopcd0:v:16:y:2026:i:1:p:1-25
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJOPCD.416703
    Download Restriction: no
    ---><---

    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:igg:jopcd0:v:16:y:2026:i:1:p:1-25. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.