Integrating Artificial Intelligence Tools in Classroom Language Assessment: Uses, Perceptions, and Pedagogical Implications
DOI:
https://doi.org/10.14456/bej.2025.18Keywords:
artificial intelligence, language assessment, classroom technology, teacher perceptions, pedagogical implicationsAbstract
This study explores the uses, perceptions, and pedagogical implications of integrating Artificial Intelligence (AI) tools in classroom language assessment among 30 Junior High School English Language teachers from public and private schools. Using a descriptive-quantitative design, data were collected through a structured survey capturing teachers’ experiences and insights. Results indicate that automated scoring software is the most commonly used AI tool, valued for its efficiency, consistency, and ability to provide prompt feedback, while less frequently used tools, such as AI speech labs, reveal opportunities to enhance oral language assessment. Teachers identified advantages including engagement, reduced bias, and adaptive learning, alongside challenges such as overreliance on technology, limited human interaction, and reduced capacity to capture nuanced student performance. Findings suggest that AI integration enhances assessment efficiency, supports higher-order thinking through authentic and adaptive tasks, and fosters multifaceted evaluation, while also reshaping teachers’ roles and highlighting the need for professional competence and ethical awareness. Effective AI implementation requires balancing technological tools with human judgment to ensure fairness, meaningful learning, and holistic assessment.
References
Adil, J. G. (2024). AI in education: A systematic literature review of emerging trends, benefits, and challenges. Seminars in Medical Writing and Education. https://doi.org/10.56294/mw2025795
Alam, A. (2022). Possibilities and apprehensions in the landscape of artificial intelligence in education. International Journal of Education and Development Using Information and Communication Technology, 18(2), 125-138.
Attali, Y., & Burstein, J. (2006). Automated essay scoring with e-rater® V.2. Journal of Technology, Learning, and Assessment, 4(3), 1-30.
Boonchom, W., Piyanukool, S., & Prachanant, N. (2024). Trends of using artificial intelligence (AI) technologies in research studies of English language teaching. BRU ELT Journal. https://doi.org/10.14456/bej.2024.3
Bulut, O., Beiting-Parrish, M., Casabianca, J. M., Slater, S. C., Jiao, H., & Song, D. (2024). The rise of artificial intelligence in educational measurement: Opportunities and ethical challenges. arXiv. https://arxiv.org/abs/2406.18900
Chapelle, C. A. (2021). Challenges in using AI for language assessment. Language Testing, 38(3), 361-371. https://doi.org/10.1177/02655322211014357
Chapelle, C. A., & Chung, Y. R. (2015). The promise of NLP and speech processing technologies in language assessment. Language Testing, 27(3), 301-315.
Chapelle, C. A., & Voss, E. (2021). The Routledge handbook of second language acquisition and language testing. Routledge.
Cui, Y., & Liang, M. (2024). Automated scoring of translations with BERT models: Chinese and English language case study. Applied Sciences, 14(5), 1925. https://doi.org/10.3390/app14051925
Fatima, K. (2025). The role of AI in teacher professional development: Implications for AI literacy and training. AI EDIFY Journal, 2(1).
Fulcher, G. (2015). Practical language testing. Routledge.
González Calatayud, V., Prendes Espinosa, P., & Roig Vila, R. (2021). Artificial intelligence for student assessment: A systematic review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/app11125467
He, L., & Yu, S. (2023). Artificial intelligence in language testing and assessment: Developments, challenges, and future directions. Language Testing in Asia, 13(1), 1-15. https://doi.org/10.1186/s40468-023-00209-4
Kaelbling, M., & Moore, C. (2021). Computational intelligence and computing applications. In 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) (pp. 1-8). IEEE. https://doi.org/10.1109/ICCICA52458.2021.9697272
Khaengkhan, A., Chaiyarak, S., Koednet, A., Thitipetchakul, C., Skunhom, V., & Iamsen, S. (2025). Language competencies for Thai teachers in the AI era. ASEAN Journal of Education, 11(2). https://so01.tci-thaijo.org/index.php/AJE/article/view/274928
Kukulska-Hulme, A., Lee, H., Norris, L., & Zhang, Y. (2022). AI for language learning: Benefits and challenges. ReCALL, 34(3), 227-243. https://doi.org/10.1017/S0958344022000039
Lu, X., & Lim, J. M. H. (2023). Adaptive testing and AI in language education: Opportunities and limitations. System, 116, 102924. https://doi.org/10.1016/j.system.2023.102924
Nguyen, T. L., Nguyen, H. L., & Le, D. H. (2024). Teachers’ perspectives on AI-driven Quillionz for generating EFL reading comprehension quizzes. In Proceedings of the AsiaCALL International Conference (Vol. 6, pp. 20-34). https://doi.org/10.54855/paic.2462
Özdere, M. (2023). The integration of artificial intelligence in English education: Opportunities and challenges. Language Education and Technology, 3(2).
Phumeechanya, N., & Wannapiroon, P. (2020). A conceptual framework for applying artificial intelligence in adaptive learning for Thai higher education. International Journal of Emerging Technologies in Learning, 15(9), 20-36. https://doi.org/10.3991/ijet.v15i09.12619
Ranalli, J., Link, S., & Chukharev-Hudilainen, E. (2022). Automated writing evaluation for formative assessment: Teacher and student perspectives. Language Learning & Technology, 26(1), 1-23.
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Sahmaniasl, R. (2025). EFL educators’ perceptions of AI-driven assessments: A systematic review. International Journal of Multidisciplinary Research and Growth Evaluation, 6(03), 672-680.
Sakmiankaew, I., Nguyen, M. T., Nguyen, N. D. D., & Buripakdi, A. (2024). EFL tertiary teachers’ and students’ conceptualizations and challenges of using AI tools to improve writing skills in Thailand and Vietnam during the Covid-19 pandemic. rEFLections, 31(3), 1120-1143. https://doi.org/10.61508/refl.v31i3.277354
Song, D., Lee, W.-C., & Jiao, H. (2025). Exploring LLM autoscoring reliability in large-scale writing assessments using generalizability theory. arXiv. https://arxiv.org/abs/2507.19980
Uyar, A. C., & Büyükahıska, D. (2025). Artificial intelligence as an automated essay scoring tool: A focus on ChatGPT. International Journal of Assessment Tools in Education, 12(1), 20-32. https://doi.org/10.21449/ijate.1517994
UNESCO. (2024). AI competency framework for teachers. UNESCO.
Weigle, S. C. (2015). Assessing writing. Cambridge University Press.
Williamson, B., & Piattoeva, N. (2022). Education governance and datafication: The role of AI in assessment policy. Learning, Media and Technology, 47(2), 213-226. https://doi.org/10.1080/17439884.2021.1942957
Winna, W., & Sabarun, S. (2023). The language assessment in teaching-learning English. DIAJAR: Jurnal Pendidikan dan Pembelajaran, 2(4), 413-419. https://doi.org/10.54259/diajar.v2i4.1894
Zhang, X. (2025). Fairness and effectiveness in AI-driven educational assessments: Challenges and mitigation strategies. Journal of Innovation and Development, 11(1), 7-10. https://doi.org/10.54097/adcxze34
Zhang, Y., & Lin, Y. (2024). Integrating AI feedback tools in English writing assessment: A teacher perspective. TESOL Quarterly, 58(1), 55-78.
