Investigating ChatGPT Integration into Iranian EFL Classrooms: A Mixed Methods Study of Personalized Language Learning
Year: 2025
Type: article
Abstract: Artificial intelligence (AI) is rapidly transforming educational landscapes, particularly in language learning, where personalized and real-time support is critical for learner success. This mixed methods study investigates how ChatGPT, as an AI-driven tool, is transforming English as a Foreign Language (EFL) classrooms in Iran, particularly in the areas of learner engagement, individualized feedback, and adaptive instruction. The study involved both learners and teachers, using a combination of interviews, classroom observations, and a Technology Acceptance Model (TAM)-based questionnaire. Interview findings indicated that both teachers and learners viewed ChatGPT as an engaging, motivating, and accessible learning assistant that enhanced autonomy and interest. Observations confirmed increased student participation during AI-integrated sessions. They highlighted the practical benefits of real-time support while revealing common implementation challenges such as unstable internet access and lack of teacher training. Survey results showed general acceptance of ChatGPT among both teachers and students, with learners showing more enthusiasm regarding its usefulness. Both groups appreciated its intuitive interface and potential to enhance learning experiences. Although the integration of AI holds great promise for Iranian EFL classrooms, the study identified critical barriers, including insufficient infrastructure, limited teacher preparedness, and institutional resistance. These findings call for targeted professional development, stronger digital infrastructure, and culturally responsive strategies to leverage AI technologies in EFL education fully.
Authors: Mohammadi, Abdolhamid, Afraz, Shahram, Samimi, Fazlolah, Alamdar, Fatemeh Sadat
Institutions: Islamic Azad University Bandar Abbas, Ministry of Education
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W6977512170
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