VOLUME 05 ISSUE 07 July 2026
Trinh Vuong Khoi
Thu Duc College of Technology
DOI : https://doi.org/10.58806/ijsshmr.2026.v5i7n01Google Scholar Download Pdf
ABSTRACT
This study investigates English language teachers' perspectives on the integration of Generative Artificial Intelligence (AI) in IELTS Writing assessment within the Vietnamese educational landscape in 2026. Employing a convergent parallel mixed-methods design, quantitative data were gathered from a survey questionnaire administered to 70 IELTS instructors, complemented by qualitative insight from 10 semi-structured in-depth interviews. Statistical testing validated four primary hypotheses: (1) teacher attitudes towards AI acceptance significantly differ based on their years of teaching experience; (2) pedagogical trust in AI is highly concentrated around mechanical metrics (grammar and lexical accuracy) but plummets regarding abstract cognitive dimensions (task response and coherence); (3) deep-seated anxieties surrounding academic integrity and the stagnation of learners' critical thinking serve as primary barriers to comprehensive adoption ; and (4) operational productivity pressures within institutional environments compel teachers to adopt AI as a professional survival mechanism. Based on these empirical findings, this paper proposes a structured "Hybrid Rater" model alongside institutional policy frameworks to optimize human-AI collaboration in high-stakes language testing.
KEYWORDS:AI ineducation, IELTS writing assessment, Teachers’ perspectives, Technology Acceptance Model (TAM), Language testing
REFERENCESBooks & Book Chapters
1) Banerjee, J. (2014). The interpretation of descriptors in the assessment of writing. Language Testing and Assessment.
2) Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate data analysis (7th ed.). Pearson Prentice Hall.
3) Halliday, M. A. K., & Hasan, R. (2014). Cohesion in English. Routledge.
4) Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition (3rd ed. draft). Pearson.
5) Landauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259–284.
6) Lee, I. (2017). Classroom writing assessment and feedback in L2 school contexts. Springer.
7) Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press.
8) Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Journal Articles
9) Bailey, D., & West, A. R. (2022). The role of artificial intelligence in English language assessment: Opportunities and challenges. Journal of Educational Technology, 45(3), 210–225. https://doi.org/10.1016/j.jet.2022.01.005
10) Barkaoui, K. (2011). Effects of marking method and rater experience on ESL essay scores and rater performance.
11) Assessment in Education: Principles, Policy & Practice, 18(3), 279–293.
12) Condon, W., Gong, G., & Guerrero, S. (2020). Consistency and reliability in automated writing evaluation systems.
13) Journal of Writing Assessment, 13(1).
14) Crossley, S. A., & McNamara, D. S. (2016). Say more and be more coherent: How text elaboration and cohesion can increase writing quality. Journal of Writing Research, 7(2), 165–191.
15) Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
16) Ertmer, P. A. (1999). Addressing first- and second-order barriers to change: Strategies for technology integration.
17) Educational Technology Research and Development, 47(4), 47–61.
18) Kim, S., & Lee, H. (2024). The impact of AI-assisted feedback on writing proficiency and learner satisfaction: An empirical study in South Korea. English Teaching.
19) Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70.
20) Lumley, T. (2002). Assessment of oral proficiency: The role of the rater. Language Testing, 19(3), 246–275.
21) Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730–27744.
22) Page, E. B. (1966). The imminence of grading essays by computer. The Phi Delta Kappan, 47(5), 238–243.
23) Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301–328.
24) Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
25) Wang, L. (2023). Mandatory acceptance? A survey of university teachers' perceptions of AI-automated grading in China. Asia Pacific Journal of Education.
26) Warschauer, M., & Grimes, D. (2008). Automated writing assessment in the classroom. Pedagogies: An International Journal, 3(1), 22–36.
27) Zhai, X. (2021). ChatGPT for next generation science learning. XRDS: Crossroads, The ACM Magazine for Students, 28(2), 38–42. https://doi.org/10.1145/3511116
Conference Proceedings & Reports
28) Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Proceedings of the 34th International Conference on Neural Information Processing Systems (NeurIPS 2020).
29) IELTS Partners. (2023). IELTS Writing Band Descriptors (Public Version).
30) Higgins, D., Burstein, J., Marcu, D., & Gentile, C. (2004). Evaluating aspect-based holistic scoring models for a computerized writing assessment. In Proceedings of the 20th international conference on Computational Linguistics (p. 233). Association for Computational Linguistics.
31) Yannakoudakis, H., & Briscoe, T. (2018). Automated writing evaluation and its feedback: Current state and future directions. Proceedings of the 13th Workshop on Innovative Use of NLP for Building Educational Applications, 124–135.
Cite this Article:Khoi, T. V. (2026). An Investigation of Teachers’ Perspectives on Utilizing Artificial Intelligence in IELTS Writing Assessment. INTERNATIONAL JOURNAL OF SOCIAL SCIENCE HUMANITY & MANAGEMENT RESEARCHS, 5(7), 1841-1848. https://doi.org/10.58806/ijsshmr.2026.v5i7n01