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

Manal A Altawalbeh () and Khaleel Al-Said ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 2, 1359-1369

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.

Keywords: Artificial Intelligence; English Lecturers; Intention; Jordan; TPACK Predicting; UTAUT. (search for similar items in EconPapers)
Date: 2025
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