Integrating Artificial Intelligence-Powered Large Language Models in English as a Foreign Language EFL Teacher Education Programs
Atieh Mohd Albadarneh, Amjad Mahmoud Daradkah, Esraa Ahmed Telfah,
Haggag Mohamed Haggag, Fadi Fuad Ghawanmeh,
Yasmin Abdullah Al-Shunnaq, Maria Salih Tawalbeh,
Ashraf Mahmoud Ahmed Mahmoud, Karima Shahine, Heba Sadiq Daradkah
Large Language Models LLMs are revolutionary advancement in Artificial Intelligence AI studies that affected different disciplines including teacher education. This research aimed to identify the current LLM oriented practices in English as a Foreign Language EFL teacher education programs in the Arabian context. The research adopts the descriptive analytical design through the application and analysis of Large Language Models questionnaire designed by the researchers. The questionnaire included (12) key LLM types and it was run over (540) pre-service EFL teachers in different Arabian teacher education contexts. Results showed that two models were reported to be highly utilized by the participants; these models were (chat models, and questioning models). Further, six models reported to be (mid), these models were (copywriting models, creation models, tuning models, inference models, research models, and exams models). In addition, four models reported to be used in (low) levels by the participants, these models were (coding models, developers' models, operation models, and hardware models). Based on the obtained results, the research recommends integrating different types of LLMs in EFL teaching education programs based on their actual performances over these models. These models include copyrighting, creation, tuning, questioning, and inference models as well; besides, considering its risks such as lack of adaptability, bias, cost and other use-oriented limitations.
Keywords: Large Language Models, Artificial Intelligence, TEFL Education
