Trustworthy Educational Outputs in Online Language Assessment: Student and Expert Views
DOI:
https://doi.org/10.24191/fa30qg48Keywords:
assessment authenticity, Generative Artificial Intelligence, human verification, online language assessment, trustworthy educational outputsAbstract
Generative Artificial Intelligence has created new challenges for online language assessment because a strong performance may not always reflect a learner’s independent ability. This study examined how students and language assessment experts viewed the trustworthiness of educational outputs in online speaking assessment. The study used a mixed-methods secondary analysis of data collected from 186 Malaysian university students who completed an Online Semi-Scripted Dialogue Assessment and five experts in language assessment and educational technology. Quantitative questionnaire data were analysed using descriptive statistics, while students’ open-ended responses and experts’ semi-structured written interview responses were analysed thematically. The findings showed that students generally viewed the online speaking assessment as clear, fair, and capable of measuring their speaking ability. However, students and experts also recognised that ChatGPT, other artificial intelligence tools, translation applications, second devices, prepared scripts, and help from other people could influence assessment performance. These forms of assistance may improve the quality of a response without providing accurate evidence of the learner’s independent language ability. The findings also highlighted the importance of clear rules, real-time interaction, spontaneous questions, oral verification, and professional judgement. The study concludes that trustworthy educational outputs in online language assessment depend on authentic performance, transparent expectations about artificial intelligence use, and meaningful human verification.
References
Bachman, L. F., & Palmer, A. S. (1996). Language testing in practice: Designing and developing useful language tests. Oxford University Press.
Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE.
Chuang, P. L., & Yan, X. (2025). Language assessment in the era of generative artificial intelligence: Opportunities, challenges, and future directions. System, 134, Article 103846. https://doi.org/10.1016/j.system.2025.103846
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
Ebrahimzadeh, M., Shibani, A., & Buckingham Shum, S. (2026). Coauthorship integrity: Reconceptualising assessment validity for the age of generative artificial intelligence. Computers and Education: Artificial Intelligence, 10, Article 100609. https://doi.org/10.1016/j.caeai.2026.100609
Hu, H., & Hashim, H. (2026). Oral Assessment in the AI Era—Equity, Validity, and Scale. Educational Researcher, 55(5), 339-340. https://doi.org/10.3102/0013189X261426370
Khan, M. M., Dong, Y., & Manesh, N. A. (2023). Authentic assessment design for meeting the challenges of generative artificial intelligence. 10.1109/FIE58773.2023.10343376
Kizilcec, R. F., Huber, E., Papanastasiou, E. C., Cram, A., Makridis, C. A., Smolansky, A., Zeivots, S., & Raduescu, C. (2024). Perceived impact of generative AI on assessments: Comparing educator and student perspectives in Australia, Cyprus, and the United States. Computers and Education: Artificial Intelligence, 7, Article 100269. https://doi.org/10.1016/j.caeai.2024.100269
Moqbel, M. S. S., & Al-Kadi, A. M. T. (2023). Foreign language learning assessment in the age of ChatGPT: A theoretical account. Journal of English Studies in Arabia Felix, 2(1), 71–84. https://doi.org/10.56540/jesaf.v2i1.62
Nelson, A. S., Santamaría, P. V., Javens, J. S., & Ricaurte, M. (2025). Students’ perceptions of generative artificial intelligence (GenAI) use in academic writing in English as a foreign language. Education Sciences, 15(5), Article 611. https://doi.org/10.3390/educsci15050611
Ozer, O. (2024). AI language models: A breach of academic integrity in online language learning? https://doi.org/10.58379/ezys5680
Su, F., O'Dea, M., Wood, M., & Seidl, E. (2026). Embracing a new world: Authentic assessment designs in an age of generative artificial intelligence (GenAI). https://doi.org/10.1080/13603108.2025.2601741
Thamdee, N., & Man-khongdi, P. (2025). Reframing communicative competence in the AI era: A conceptual framework for human AI communication in EFL contexts. International Journal of Research in Social Science and Humanities, 6(12), 87–99. https://doi.org/10.47505/IJRSS.2025.12.6
Valkova, R. B. (2024). Remote proctoring in language assessment: Exploring the impact on test-takers' scores and perceptions. https://doi.org/10.58379/fczm2855
Vellanki, S. S., Mond, S., & Khan, Z. (2023). Promoting academic integrity in remote/online assessment — EFL teachers' perspectives. https://doi.org/10.55593/ej.26104a7
Voss, E., Cushing, S. T., Ockey, G. J., & Yan, X. (2023). The use of assistive technologies including generative AI by test takers in language assessment: A debate of theory and practice. Language Assessment Quarterly, 20(4–5), 520–532. https://doi.org/10.1080/15434303.2023.2288256
Weaver, K. D. (2026). Artificial intelligence and transparency: Toward a framework for disclosure of AI use in learning, research, and publication. https://doi.org/10.1177/18758789261435716
Xi, X. (2023). Advancing language assessment with AI and ML: Leaning into AI is inevitable, but can theory keep up? Language Assessment Quarterly, 20(4–5), 357–376. https://doi.org/10.1080/15434303.2023.2291488
Xi, X. (2025). Revisiting communicative competence in the age of AI: Implications for large scale testing. Annual Review of Applied Linguistics, 45, 200–221. https://doi.org/10.1017/S0267190525000078
Yusuf, A., Pervin, N., & Román González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21, Article 21. https://doi.org/10.1186/s41239-024-00453-6
Zhang, D., Hoang, T., Pan, S., Hu, Y., Xing, Z., Staples, M., Xu, X., Lu, Q., & Quigley, A. (2023). Test takers have a say: Understanding the implications of the use of AI in language tests [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2307.09885
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