AI in English Language Teaching at Higher Education Institutions in Georgia: Usage, Training Gaps, and Institutional Support
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| Title: | AI in English Language Teaching at Higher Education Institutions in Georgia: Usage, Training Gaps, and Institutional Support |
|---|---|
| Language: | English |
| Authors: | Salome Gogberashvili (ORCID |
| Source: | JALT CALL Journal. 2026 22(1). |
| Availability: | JALT CALL SIG. 1-6-1 Nishiwaseda Shinjuku-ku, Tokyo, 169-8050, Japan. e-mail: journal!jaltcall.org; Web site: https://jaltcall.org |
| Peer Reviewed: | Y |
| Page Count: | 19 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, English (Second Language), Second Language Instruction, Higher Education, Foreign Countries, Teaching Methods, Barriers, Technology Integration, Faculty Development, College Faculty, Teacher Attitudes |
| Geographic Terms: | Georgia Republic |
| ISSN: | 1832-4215 |
| Abstract: | The integration of Artificial Intelligence (AI) in English language teaching within Georgian higher education institutions presents both new opportunities and challenges. A survey was conducted among 104 university educators throughout Georgia. The findings revealed that while AI adoption is growing, significant gaps in training and support hinder effective implementation. This study examines the correlation between AI training and the effective adoption of AI tools by university educators, assessing its impact on teaching practices, student engagement, and institutional support structures. The findings indicate that instructors who have received AI training demonstrate greater proficiency, utilizing AI for tasks such as grading, syllabus design, and adaptive learning. Additionally, trained educators are more likely to guide students in AI applications. Challenges include limited funding, technical difficulties, and confidence gaps in AI usage. The study underscores the urgent need for structured AI training programs, ongoing professional development, and institutional policies that facilitate AI adoption. Both trained and untrained instructors highlight the lack of institutional training and support as significant barriers to AI integration in teaching practices, limiting their ability to effectively implement AI in the classroom and guide students in its use. By identifying training gaps and institutional support mechanisms, this research provides insights into optimizing AI-driven teaching approaches in higher education. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1506391 |
| Database: | ERIC |
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