AI-powered EFL pedagogy: Integrating generative AI into university teaching preparation through UTAUT and activity theory

This study explores the integration of generative AI into English as a Foreign Language (EFL) teaching preparation within Indonesian higher education, addressing the growing need to understand how emerging technologies can enhance pedagogical practices in a rapidly evolving educational landscape. By employing the Unified Theory of Acceptance and Use of Technology (UTAUT) and Activity Theory, the research provides a robust analytical framework to examine the factors influencing lecturers' adoption of generative AI. The study is particularly relevant as generative AI offers significant potential to improve teaching efficiency and content personalization, yet its adoption presents challenges in aligning outputs with educational standards and maintaining meaningful teacher-student interaction. Using a mixed-methods approach, the research combined quantitative data from structured questionnaires with qualitative insights from reflective compositions, where lecturers critically evaluated their experiences with generative AI. Structural Equation Modeling (SEM) revealed that performance expectancy and social influence significantly and positively influenced behavioral intention, while effort expectancy had no significant effect. Facilitating conditions, unexpectedly, negatively impacted behavioral intention, likely due to satisfaction with existing resources reducing the perceived necessity for new tools. A strong positive correlation between behavioral intention and actual use behavior demonstrated the critical role of intention in driving adoption. Thematic analysis provided further depth by emphasizing both the benefits and challenges of generative AI, accentuating the importance of balancing its use with human instruction to ensure quality teaching and interaction. The study stresses the need for the strategic integration of generative AI, offering practical and theoretical insights into its adoption and implications for advancing EFL teaching in higher education.

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Publication Details

Journal
Computers and Education Artificial Intelligence
Published
2024-12-01
DOI
https://doi.org/10.1016/j.caeai.2024.100335
Citations
46
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
18.94
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article

AI-powered EFL pedagogy: Integrating generative AI into university teaching preparation through UTAUT and activity theory

Mei Hardiah, Havid Ardi, Safnil Arsyad, Muhd. Al Hafizh et al.
46 citations
Computers and Education Artificial Intelligence
Online Learning and Analytics
18.94
article

AI-powered EFL pedagogy: Integrating generative AI into university teaching preparation through UTAUT and activity theory

Mei Hardiah, Havid Ardi, Safnil Arsyad, Muhd. Al Hafizh, Ahmad Nusi, M. Zaim, Widya Syafitri, Muflihatuz Zakiyah, Budi Waluyo
article en
46 citations

Abstract

This study explores the integration of generative AI into English as a Foreign Language (EFL) teaching preparation within Indonesian higher education, addressing the growing need to understand how emerging technologies can enhance pedagogical practices in a rapidly evolving educational landscape. By employing the Unified Theory of Acceptance and Use of Technology (UTAUT) and Activity Theory, the research provides a robust analytical framework to examine the factors influencing lecturers' adoption of generative AI. The study is particularly relevant as generative AI offers significant potential to improve teaching efficiency and content personalization, yet its adoption presents challenges in aligning outputs with educational standards and maintaining meaningful teacher-student interaction. Using a mixed-methods approach, the research combined quantitative data from structured questionnaires with qualitative insights from reflective compositions, where lecturers critically evaluated their experiences with generative AI. Structural Equation Modeling (SEM) revealed that performance expectancy and social influence significantly and positively influenced behavioral intention, while effort expectancy had no significant effect. Facilitating conditions, unexpectedly, negatively impacted behavioral intention, likely due to satisfaction with existing resources reducing the perceived necessity for new tools. A strong positive correlation between behavioral intention and actual use behavior demonstrated the critical role of intention in driving adoption. Thematic analysis provided further depth by emphasizing both the benefits and challenges of generative AI, accentuating the importance of balancing its use with human instruction to ensure quality teaching and interaction. The study stresses the need for the strategic integration of generative AI, offering practical and theoretical insights into its adoption and implications for advancing EFL teaching in higher education.

Computers and Education Artificial IntelligenceVol. 7
State University of Padang (ID), University of Bengkulu (ID), Walailak University (TH)
Openalex Percentile: Top 1%
Online Learning and Analytics
18.94
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