IDEAS home Printed from https://ideas.repec.org/a/aac/ijirss/v8y2025i2p1359-1369id5464.html
   My bibliography  Save this article

Applying UTAUT and TPACK in predicting English lecturers' intention to use artificial intelligence

Author

Listed:
  • Manal A Altawalbeh
  • Khaleel Al-Said

Abstract

As AI technologies with predictive capabilities increasingly spread, it has become necessary to leverage them in light of the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technological Pedagogical Content Knowledge (TPACK) frameworks, especially in the English language. This study investigates the factors influencing English lecturers’ intentions to adopt artificial intelligence (AI) in teaching, utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technological Pedagogical Content Knowledge (TPACK) frameworks. A quantitative research methodology was employed, collecting data from 174 English lecturers in Jordan through structured questionnaires. Structural Equation Modeling (SEM) was used to analyze the relationships between UTAUT constructs—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)—and TPACK components, including Technological Knowledge (TK), Pedagogical Knowledge (PK), and Content Knowledge (CK). Reliability and validity measures confirmed the robustness of the instrument. The findings reveal that PE, EE, SI, and FC significantly predict lecturers’ Behavioral Intention (BI) to adopt AI tools. Furthermore, TPACK components, particularly Technological Pedagogical Knowledge (TPK) and Technological Content Knowledge (TCK), mediate the relationship between UTAUT factors and BI. Facilitating Conditions and Social Influence were found to have the strongest indirect impact through TPACK constructs. The model fit indices indicated a good fit, validating the proposed hypotheses. The study underscores the importance of professional development programs to enhance educators’ TPACK and emphasizes the need for institutional support to foster AI adoption. These findings contribute to the literature on technology adoption in education and provide actionable recommendations for integrating AI into English language teaching.

Suggested Citation

  • Manal A Altawalbeh & Khaleel Al-Said, 2025. "Applying UTAUT and TPACK in predicting English lecturers' intention to use artificial intelligence," International Journal of Innovative Research and Scientific Studies, Innovative Research Publishing, vol. 8(2), pages 1359-1369.
  • Handle: RePEc:aac:ijirss:v:8:y:2025:i:2:p:1359-1369:id:5464
    as

    Download full text from publisher

    File URL: https://ijirss.com/index.php/ijirss/article/view/5464/938
    Download Restriction: no
    ---><---

    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:aac:ijirss:v:8:y:2025:i:2:p:1359-1369:id:5464. 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: Natalie Jean (email available below). General contact details of provider: https://ijirss.com/index.php/ijirss/ .

    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.