1- Department of English language teaching, Farhangian University, Tehran, Iran , mohammadrezatusi1@gmail.com
2- Ministry of Education, Tehran, Iran
3- Ministry of Education, Tehran, Iran
2- Ministry of Education, Tehran, Iran
3- Ministry of Education, Tehran, Iran
Abstract: (254 Views)
Objective: This study aims to explore the degree of AI literacy among Iranian pre-service EFL teachers at Zanjan University and provide insights into its key components for future pedagogical planning.
Method: Given the descriptive, quantitative nature of the study, a five-point Likert scale questionnaire was used to assess AI literacy across six constructs among 53 participants in a teacher education program (15 male, 38 female). The instrument was developed by Ayanwale et al. (2024) and was re-tested again for ensuring its reliability and validity in our own specific research context.
Results: The results indicated higher levels of AI literacy in lower-order cognitive skills (according to Blooms taxonomy) such as “use and apply AI” and “know and understand AI”, and lower levels in higher-order skills like; “create AI” and “detect AI”. Furthermore, the analysis demonstrated that “know and understand AI” construct was a significant predictor of overall AI literacy, followed by “AI emotion regulation” and “use and apply”. The study also compared AI literacy levels based on prior AI training, and identified the perceived challenges of applying AI tools in education.
Conclusions: Pre-service EFL teachers demonstrated uneven AI literacy, with stronger performance in foundational skills and weaker in advanced competencies. These results highlight the need for targeted teacher education initiatives to enhance higher-order AI literacy skills and address barriers to effective classroom integration.
Method: Given the descriptive, quantitative nature of the study, a five-point Likert scale questionnaire was used to assess AI literacy across six constructs among 53 participants in a teacher education program (15 male, 38 female). The instrument was developed by Ayanwale et al. (2024) and was re-tested again for ensuring its reliability and validity in our own specific research context.
Results: The results indicated higher levels of AI literacy in lower-order cognitive skills (according to Blooms taxonomy) such as “use and apply AI” and “know and understand AI”, and lower levels in higher-order skills like; “create AI” and “detect AI”. Furthermore, the analysis demonstrated that “know and understand AI” construct was a significant predictor of overall AI literacy, followed by “AI emotion regulation” and “use and apply”. The study also compared AI literacy levels based on prior AI training, and identified the perceived challenges of applying AI tools in education.
Conclusions: Pre-service EFL teachers demonstrated uneven AI literacy, with stronger performance in foundational skills and weaker in advanced competencies. These results highlight the need for targeted teacher education initiatives to enhance higher-order AI literacy skills and address barriers to effective classroom integration.
Keywords: AI literacy, Pre-service EFL teachers, Artificial intelligence in education, Teacher training, Language education
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