Utilizing Artificial Intelligence in Writing Feedback: Benefits and Challenges for First-Year Students at Hanoi University of Industry
DOI:
https://doi.org/10.54855/paic.24617Keywords:
Artificial Intelligence, writing feedback, potentials, challenges, benefitsAbstract
This paper analyses the application of AI technology to the process of delivering writing feedback to first-year students of Hanoi University of Industry (HaUI). AI brings an effective solution to improve writing skills since one wants to receive individual and effective instruction. In this article, the authors discuss how there are opportunities for the use of AI to improve feedback quality, time, and flexibility in line with the various AI tools and platforms that cater to young writers. It also explores the implications and difficulty of incorporating AI-driven feedback systems for the classroom, like having issues with technology use and the roles and participation of teachers. This research uses qualitative data collection techniques, interviewing 10 teachers and focusing group discussions with 50 students majoring in business. Drawing on the analytical framework outlined above, this article examines the possible benefits and limitations of AI written feedback applicable to HaUI and offers directions for teachers and administrators in comparable contexts who want to utilize AI technology for writing feedback to support learners.References
Alharbi, M. A., & Al-Hoorie, A. H. (2020). Turnitin peer feedback: Controversial vs. non-controversial essays. International Journal of Educational Technology in Higher Education, 17(1), 17. DOI: https://doi.org/10.1186/s41239-020-00195-1
Almusharraf, N., & Alotaibi, H. (2023). An error-analysis study from an EFL writing context: Human and automated essay scoring approaches. Technology, Knowledge and Learning, 28(3), 1015–1031. https://doi.org/10.1007/s10758-022-09592-z DOI: https://doi.org/10.1007/s10758-022-09592-z
Bali, M. (2017). Against the 3A’s of EdTech: AI, analytics, and adaptive technologies in education. The Chronicle of Higher Education. Available at: https://www.chronicle.com/blogs/profhacker/against-the-3as-of-edtech-ai-analytics-and-adaptive-technologies-in-education/64604
Cahyono, B. Y., & Kurniawan, D. A. (2020). Evaluating the effectiveness of an AI-powered assessment tool for writing. Journal of English Language Teaching and Linguistics, 5(2), 217–232.
Dong, Y. (2023). Revolutionizing academic English writing through AI-powered pedagogy: Practical exploration of teaching process and assessment. Journal of Higher Education Research, 4(2), 52–57. DOI: https://doi.org/10.32629/jher.v4i2.1188
Duong, N. H., Tong, T. M. H., & Le, D. H. (2024). Utilizing ChatGPT in checking academic writing for postgraduate students. Proceedings of the Asia CALL International Conference, 6,193-203.ISSN: 2833-6836, ISBN: 979-8-9870112-6-3. DOI: https://doi.org/10.54855/paic.24614 DOI: https://doi.org/10.54855/paic.24614
Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial intelligence–enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684 DOI: https://doi.org/10.1016/j.compedu.2022.104684
Huang, W., Hew, K. F., & Gonda, D. E. (2019). Designing and evaluating three chatbot-enhanced activities for a flipped graduate course. International Journal Mechanical Engineering and Robotics Research, 8, 813–818. https://doi.org/10.18178/ijmerr.8.5.813-818 DOI: https://doi.org/10.18178/ijmerr.8.5.813-818
Klein, E., O'Connor, B., & Cosmides, L. (2019). Bias in, bias out: Assessing the presence of racial and gender bias in a natural language processing platform. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 5799–5804).
Laflen, A. (2023). Exploring how response technologies shape instructor feedback: A comparison of Canvas Speed grader, Google Docs, and Turnitin Grade Mark. Computers and Composition, 68, 102777. DOI: https://doi.org/10.1016/j.compcom.2023.102777
Li, B., & Peng, M. (2022). Integration of an AI-based platform and flipped classroom instructional model. Scientific Programming, 2022, 2536382. https://doi.org/10.1155/2022/2536382 DOI: https://doi.org/10.1155/2022/2536382
Liu, X., Xu, J., Xu, Y., & Liu, B. (2020). An intelligent writing assistant for argumentation skills development. IEEE Transactions on Learning Technologies, 13(4), 573–586.
Lo, C. K., & Hew, K. F. (2023). A review of integrating AI-based chatbots into flipped learning: New possibilities and challenges. Frontiers in Education, 8, 1175715. https://doi.org/10.3389/feduc.2023.1175715 DOI: https://doi.org/10.3389/feduc.2023.1175715
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. London: Pearson.
Maudilidina, P., & Wibowo, H. (2022). The use of Grammarly tools to enrich students’ writing ability. Lingua: Journal Pendidikan Bahasa, 18(2), 179–189. DOI: https://doi.org/10.34005/lingua.v18i2.2246
Miranty, D., & Widiati, U. (2021). An automated writing evaluation (AWE) in higher education. Pegem Journal of Education and Instruction, 11(4), 126–137. https://doi.org/10.47750/pegegog.11.04.12 DOI: https://doi.org/10.47750/pegegog.11.04.12
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI‐powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13232 DOI: https://doi.org/10.1111/bjet.13232
Owan, V., Abang, K., Idika, D., & Bassey, B. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19, Article ID em2307. https://doi.org/10.29333/ejmste/13428. DOI: https://doi.org/10.29333/ejmste/13428
Sanosi, A. B. (2022). The impact of automated written corrective feedback on EFL learners’ academic accuracy. The Journal of Teaching English for Specific and Academic Purposes, 7(2), 301–317. https://doi.org/10.22190/JTESAP2202301S DOI: https://doi.org/10.22190/JTESAP2202301S
Tran, T. T. H. (2024). AI Tools in Teaching and Learning English Academic Writing Skills. Proceedings of the AsiaCALL International Conference, 4, 170–187. https://doi.org/10.54855/paic.23413 DOI: https://doi.org/10.54855/paic.23413
Xiong, Z., Ma, Y., Wu, Y., & Liu, J. (2020). Automatic essay grading using machine learning. International Journal of Emerging Technologies in Learning (iJET, 15(17), 63–78.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Duong Thi Kim Hue, Le Thi Thu Huong
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright
The copyright of all articles published in the Proceedings of the AsiaCALL International Conference (paic) remains with the Authors, i.e. Authors retain full ownership of their article. Permitted third-party reuse of the open access articles is defined by the applicable Creative Commons (CC) end-user license which is accepted by the Authors upon submission of their paper. All articles in the aicp are published under the CC BY-NC 4.0 license, meaning that end users can freely share an article (i.e. copy and redistribute the material in any medium or format) and adapt it (i.e. remix, transform and build upon the material) on the condition that proper attribution is given (i.e. appropriate credit, a link to the applicable license and an indication if any changes were made; all in such a way that does not suggest that the licensor endorses the user or the use) and the material is only used for non-commercial purposes.