AI Literacy in Higher Education: Transformative Impacts on EFL Departments and Teaching Practices
This study aims to discuss how AI literacy affects EFL departments in terms of curriculum, the approach to teaching, and the accomplishment of students. As innovative technologies raise fundamental challenges and opportunities for diverse educational contexts, it is critical for the current and future educators and students to be AI literate. This research uses a qualitative research approach with emphasis on the use of literature, and case studies on the incorporation of AI in EFL settings. Information was collected from peer-reviewed journals, reputable academic databases, and institutional sources, such as the studies on the application of artificial intelligence in language acquisition, the difficulties of AI adoption, and case studies. The study shows that the current level of AI awareness among many EFL departments is relatively low but there is developing acceptance of the positive role of AI in teaching and learning processes. Chatbots, language models, and adaptively intelligent learning tools are valuable for the personalized assistance, enhanced student interaction, and new approaches to languages’ challenges. However, there are also several challenges such as educators’ resistance, ethical issues, and inequality in terms of access to technology identified by the study. These concerns are illustrated through case examples of the need for institutional support, professional development for educators, and curriculum changes to enhance the use of AI. Thus, increasing an AI literacy of undergraduates can be regarded as a major trend in the further development of EFL education that should become the focus of attention for educators, policymakers, and curriculum developers. Building AI literacy and meeting implementation barriers empower learning, critical thinking and improve EFL departments for student learning for the emerging digital platforms.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-12-30
- DOI
- https://doi.org/10.5281/zenodo.14575606
- Primary Topic
- AI in Service Interactions
- Type
- article
- Field-Weighted Citation Impact
- 0.00