Exploring EFL university teachers’ perceptions of AI-generative tools in Saudi Arabia: a mixed-methods study

The birth of AI-generative tools must support English as a foreign language (EFL) teachers, but their effective integration into education remains a challenge. Therefore, the study aims to identify language teachers’ perceptions of using AI-generative tools in improving teaching. In this descriptive-survey method, an explanatory mixed methods research design was used; (84) EFL multicultural teachers completed a 30-item closed-questionnaire of three domains: material design and planning, teaching methods, and assessment and feedback. Then, the respondents answered open questions to reflect on their answers to the quantitative part. According to the findings, language teachers believe that employing AI-generative technologies has a major influence on enhancing EFL education material design and planning, teaching methods, and assessment and feedback. In addition, the results indicate a lack of influence of gender, teaching experience, and mastery level of AI-generative tools, suggesting a shared perspective among teachers. Finally, teachers perceive AI-generative tools as valuable complements to traditional English language teaching, offering opportunities for enhanced assessment, personalized learning, and engagement, while also presenting challenges related to accuracy, ethical use, and potential over-reliance. Overall, the study highlights both the potential and challenges of AI in EFL teaching, offering insights for future research and practice.

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Publication Details

Journal
Discover Computing
Published
2025-11-28
DOI
https://doi.org/10.1007/s10791-025-09793-x
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.65

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article

Exploring EFL university teachers’ perceptions of AI-generative tools in Saudi Arabia: a mixed-methods study

Ali S. Alelaiwi, Ali Abbas Falah Alzubi
1 citations
Discover Computing
Artificial Intelligence in Healthcare and Education
0.65
article

Exploring EFL university teachers’ perceptions of AI-generative tools in Saudi Arabia: a mixed-methods study

Ali S. Alelaiwi, Ali Abbas Falah Alzubi
article en
1 citations

Abstract

The birth of AI-generative tools must support English as a foreign language (EFL) teachers, but their effective integration into education remains a challenge. Therefore, the study aims to identify language teachers’ perceptions of using AI-generative tools in improving teaching. In this descriptive-survey method, an explanatory mixed methods research design was used; (84) EFL multicultural teachers completed a 30-item closed-questionnaire of three domains: material design and planning, teaching methods, and assessment and feedback. Then, the respondents answered open questions to reflect on their answers to the quantitative part. According to the findings, language teachers believe that employing AI-generative technologies has a major influence on enhancing EFL education material design and planning, teaching methods, and assessment and feedback. In addition, the results indicate a lack of influence of gender, teaching experience, and mastery level of AI-generative tools, suggesting a shared perspective among teachers. Finally, teachers perceive AI-generative tools as valuable complements to traditional English language teaching, offering opportunities for enhanced assessment, personalized learning, and engagement, while also presenting challenges related to accuracy, ethical use, and potential over-reliance. Overall, the study highlights both the potential and challenges of AI in EFL teaching, offering insights for future research and practice.

Discover ComputingVol. 28(1)
Najran University (SA)
Najran University
Openalex Percentile: Top 19%
Artificial Intelligence in Healthcare and Education
0.65
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Exploring EFL university teachers’ perceptions of AI-generative tools in Saudi Arabia: a mixed-methods study — Ali S. Alelaiwi, Ali Abbas Falah Alzubi · Discover Computing (2025) | TGRS Research Map | TGRS