Utilizing Artificial Intelligence Technologies in Saudi EFL Tertiary Level Classrooms
Open accessAbstract
This study focuses on the employment of AI technology in regular, day-to-day activities, such as when Google Translate or Bing Translator are encouraged alongside various programs and applications. It also evaluates and empirically demonstrates the subjects of writing with AI technologies, computer-assisted language learning (CALL), machine translation (MT), and automatic evaluation systems (AESs) in order to offer solutions for enhanced communication training in Saudi Arabia's EFL system. Word tune is an artificial intelligence (AI)-driven writing assistant that can understand the writer's ideas and suggest alternative rewrites (e.g., shorten, expand). This program assists writers of English as a foreign language to maintain a steady flow and acquire useful English expressions. This research made use of questionnaires as a method for collecting data and then ran those responses through SPSS for analysis. The use of artificial intelligence (AI) technology in English as a foreign language (EFL) settings has been shown to facilitate the English language learning (ELT) process and to keep both teachers and students up to date on recent technological developments. This exploratory investigation demonstrated that all digital and AI-powered devices have the potential to assist in teaching and learning. Consequently, the pedagogical component of future education can be developed using an AI framework.
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Exploring the Feedback Quality of an Automated Writing Evaluation System Pigai
Gao, J. (2021) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v16i11.19657 · Cited by 68 · see citing works
Flagged first, then by citations
Research works printed but not linked: 20
- Ahn, T. Y., & Lee, S.-M. (2016). The user experience of a mobile speaking application with automatic speech recognition for EFL learning. Br. J. Educ. Technol. 47(4), 778-786. https://doi.org/10.1111/bjet.12354 · DOI
- Arnold, D. (2003). Why translation is difficult for computers. Computers and Translation: A translator's guide, Amsterdam and Philadelphia: John Benjamins, pp. 119-42.
- Bai, L., and Hu, G. (2017). In the face of fallible awe feedback: How do students respond? Educational Psychology. 37(1), 67-81. https://doi.org/10.1080/01443410.2016.122327 · DOI
- Davis, R. (2006). Utopia or chaos? The impact of technology on language teaching. The Internet TESL Journal, 12(11), 3-4.
- Du, Y., & Gao, H. (2022). Determinants affecting teachers' adoption of ai-based applications in EFL context: an analysis of analytic hierarchy process. Educ. Inf. Technol. 27(7), 9357-9384. https://doi.org/10.1007/s10639-022-11001-y · DOI
- Hamouma, C., & Menezla, N. (2019). The impact of digital literacy proficiency on EFL students' academic writing performance: A case study of Algerian third-year EFL students. International Journal of Digital Literacy and Digital Competence, 10(4), 40-55. https://doi.org/10.4018/IJDLDC.2019100103 · DOI
- Kay, M. (1997). The proper place of men and machines in language translation. machine translation, 12(1), 3-23.
- Kessler, M. (2020). Technology-mediated writing: Exploring incoming graduate students L2 writing strategies with Activity Theory. Computers and Composition, 55(6), 102542. https://doi.org/10.1016/j.compcom.2020.102542 · DOI
- Kirschenbaum, M. (2017). Track changes: A literary history of word processing. Cambridge, MA and London, England: Harvard University Press. https://doi.org/10.4159/9780674969469 · DOI
- Koehn, P. (2004, July). Statistical significance tests for machine translation evaluation. In Proceedings of the 2004 conference on empirical methods in natural language processing (pp. 388-395).
- Liu, S., and Kunnan, A. J. (2016). Investigating the application of automated writing evaluation to Chinese undergraduate English majors: a case study of " write to learn ."Calico J. 33(1), 71-91. https://doi.org/10.1558/CJ.v33i1.26380 · DOI
- McCrocklin, S. (2019). Learners' feedback regarding asr-based dictation practice for pronunciation learning. Calico J. 36(2), 119. https://doi.org/10.1558/CJ.34738 · DOI
- Moore, K. A., Rutherford, C., & Crawford, K. A. (2016). Supporting postsecondary English language learners' writing proficiency using technological tools. Journal of International Students, 6(4), 857-872. https://doi.org/10.32674/jis.v6i4.321 · DOI
- Niess, M. L. (2011). Investigating TPACK: Knowledge growth in teaching with technology. Journal of Educational Computing Research, 44(3), 299-317. https://doi.org/10.2190/EC.44.3.c · DOI
- Perry, F. (2021). The use of embedded digital tools to develop English language proficiency in higher education. Journal of Academic Language and Learning, 15(1), 1-12.
- Qian, L., Yang, Y., & Zhao, Y. (2021). Syntactic complexity revisited: sensitivity of China's AES-generated scores to syntactic measures, effects of discourse mode and topic. Read. Writ. 34(3), 681-704. https://doi.org/10.1007/s11145-020-10087-5 · DOI
- Salaberry, M. (2001). The use of technology for second language learning and teaching: A retrospective. The Modern Language Journal, 85 (1), 39. https://doi.org/10.1111/0026-7902.00096 · DOI
- Tight, D. G. (2017). Tool usage and effectiveness among L2 Spanish computer writers. Estudios de lingüística inglesa aplicada, 17, 157-182. https://doi.org/10.12795/elia.2017.i17.07 · DOI
- Warschauer, M., & Healey, D. (1998). Computers and language learning: An overview. Language Teaching, 31(2), 57-71. https://doi.org/10.1017/S0261444800012970 · DOI
- Zitouni, K. S. (2022). New trends in EFL online learning and teaching through the lens of artificial intelligence. Almuqadimah Hum. Soc. Stud. J. 7(1), 1065-1080.
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Mahmud, F. A. (2023) · Journal of Language Teaching and Research · doi:10.17507/jltr.1405.28 · Cited by 45 · see citing works
Othman, N. A. (2025) · TESOL Today · doi:10.53898/tesol2025215 · Cited by 2 · see citing works
- Authors
- AbdAlgane, M., & Othman, K. A. J.
- Affiliations
- Qassim University (SA)
- Venue
- Journal of Intercultural Communication
- Published
- 2023-03-08
- Pages
- 92-99
- ISSN
- 1404-1634 (ISSN-L)
- OpenAlex
- W4324324063
- Cited by
- 72
- References
- 1
- Citations by year
- 2026: 19 · 2025: 23 · 2024: 25 · 2023: 5
- FWCI
- 13.03
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Intercultural Communication (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › English Language Learning and Teaching
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Keywords
- AI-assisted language learning, EFL writing, machine translation, computer-assisted language learning, AI writing tools, English as a foreign language teaching
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