EFL Students’ Perceptions of AI-Based Automated Self-Assessment in Academic Writing
DOI:
https://doi.org/10.62775/edukasia.v6i1.1447Keywords:
Academic Writing; EFL; Self-AssessmentAbstract
This study explores English as a Foreign Language (EFL) learners’ perceptions regarding the use of AI-based automated self-assessment tools and other writing technologies to improve grammatical accuracy and writing quality in English writing assignments. This study was conducted using qualitative methods. Data were collected through semi-structured interviews with five master’s students majoring in English education at Yogyakarta State University. They were selected by purposive sampling based on their experience using AI-based tools such as Grammarly and ProWritingAid. The data were then analyzed using thematic analysis, which involved several stages, including data recognition, initial coding, theme search, theme review, and theme definition and naming. This study shows that there are five challenges of using AI-based automated self-assessment tools. These are including context and interpretation errors, difficulty understanding feedback, dependence on paid features, lots of auto-feedback, and technology limitations. In the other hand, there are four benefits of using AI-based automated self-assessment. These are including increased self-confidence, efficiency in writing, independent learning, and better writing quality. This study contributes to the ongoing discussion about the role of technology in language learning, with a focus on how technology influences student learning outcomes, motivation, and reliance on automated feedback.
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References
Aspers, P., & Corte, U. (2019). What is qualitative in qualitative research? Qualitative Sociology, 42(2), 139–160. https://doi.org/10.1007/s11133-019-9413-7
Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
Birt, L., Scott, S., Cavers, D., Campbell, C., & Walter, F. (2016). Member checking: A tool to enhance trustworthiness or merely a nod to validation? Qualitative Health Research, 26(13), 1802–1811. https://doi.org/10.1177/1049732316654870
Black, P., & Wiliam, D. (2018). Classroom assessment and pedagogy. Assessment in Education: Principles, Policy & Practice, 25(6), 551–575. https://doi.org/10.1080/0969594X.2018.1441807
Boud, D., Ajjawi, R., Dawson, P., & Tai, J. (2018). Developing evaluative judgement in higher education: Assessment for knowing and producing quality work. Routledge.
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications.
------------------------------(2022). Conceptual and design thinking for thematic analysis. Qualitative Psychology, 9(1), 3–26. https://doi.org/10.1037/qup0000196
Cardona, D. A., Salazar, I. B., & Cano, M. C. (2023). Self-assessment in EFL writing: Promoting learner autonomy in higher education. Journal of Language and Education, 9(1), 24–35. https://doi.org/10.17323/jle.2023.13518
Flick, U. (2023). Introduction to qualitative research (7th ed.). SAGE Publications.
Gao, R., Merzdorf, H. E., Anwar, S., Hipwell, M. C., & Srinivasa, A. R. (2024). Automatic assessment of text-based responses in post-secondary education: A systematic review. Computers and Education: Artificial Intelligence, 6, 100206. https://doi.org/10.1016/j.caeai.2024.100206
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13. https://doi.org/10.1177/1609406917733847
Nunes, A., Cordeiro, C., Limpo, T., & Castro, S. L. (2022). Effectiveness of automated writing evaluation systems in school settings: A systematic review of studies from 2000 to 2020. Journal of Computer Assisted Learning, 38(2), 599–620. https://doi.org/10.1111/jcal.12635
Sailer, M., Stadler, M., Schultz-Pernice, F., Franke, U., & Fischer, F. (2021). Technology-related teaching skills and attitudes: Validation of a scenario-based self-assessment instrument for teachers. Computers in Human Behavior, 115, 106625. https://doi.org/10.1016/j.chb.2020.106625
Saito, H., & Inoue, T. (2019). Validity of automated writing evaluation: Current evidence and future directions. Assessing Writing, 39, 1–12. https://doi.org/10.1016/j.asw.2018.11.001
Silverman, D. (2021). Interpreting qualitative data (6th ed.). SAGE Publications.
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31, 261–292. https://doi.org/10.1007/s10648-019-09465-5
Tang, L., Shen, K., Le, H., Shen, Y., Tan, S., Zhao, Y., & Fan, Y. (2024). Facilitating learners’ self-assessment during formative writing tasks using a writing analytics toolkit. Journal of Computer Assisted Learning, 40(6), 2822–2839. https://doi.org/10.1111/jcal.12952
Van Reybroeck, M., Penneman, J., Vidick, C., & Galand, B. (2017). Progressive treatment and self-assessment: Effects on students’ automatisation of grammatical spelling and self-efficacy beliefs. Reading and Writing, 30(8), 1965–1985. https://doi.org/10.1007/s11145-017-9763-5
Wang, X., Li, H., & Wang, Y. (2022). Exploring the effectiveness of automated writing evaluation in EFL writing: A meta-analysis. System, 106, 102759.
https://doi.org/10.1016/j.system.2022.102759
Zimmerman, B. J. (2020). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
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