Students’ Perceptions and Digital Literacy Self-Efficacy Toward AI-Based Writing Feedback Systems in English Language Learning
DOI:
https://doi.org/10.51903/hj8yv131Keywords:
AI-based feedback, digital literacy, self-efficacy, academic writing, English language learningAbstract
This study examined students’ perceptions and digital literacy self-efficacy toward AI-based writing feedback systems in an English language learning context at a technology-oriented higher education institution. The increasing use of automated writing tools, such as grammar checkers and AI writing assistants, has encouraged students to rely on machine-generated feedback during early revision stages. However, their ability to interpret and apply AI suggestions varies depending on their digital literacy and evaluative skills. An explanatory sequential mixed-method design was employed, beginning with a quantitative survey followed by qualitative interviews. Seventy-two students completed the questionnaire, and ten participated in semi-structured interviews. Quantitative results indicated generally positive perceptions of AI feedback systems (M = 3.89, SD = 0.54), particularly in ease of use and initial revision support. Digital literacy self-efficacy was moderately high (M = 3.74, SD = 0.49), although confidence in distinguishing between accurate and inaccurate recommendations was lower. A weak positive correlation (r = 0.32) was found between perception and self-efficacy. Interview findings supported the survey results and highlighted three themes: AI as an initial correction tool, the need for contextual interpretation, and cautious reliance on AI-generated suggestions. Overall, the study emphasises the importance of integrating AI literacy and critical evaluation skills into writing instruction to ensure that AI-based feedback functions effectively as a supportive, rather than substitutive, tool in academic writing.
References
Alharbi, W. (2023). AI in the Foreign Language Classroom: A Pedagogical Overview of Automated Writing Assistance Tools. Education Research International, 2023(1), 4253331. https://doi.org/10.1155/2023/4253331
Amiruddin, Baharuddin, F. R., Takbir, Setialaksana, W., & Nurlaela. (2023). Andragogy, Peeragogy, Heutagogy and Cybergogy Contribution on Self-Regulated Learning: A Structural Equation Model Approach. International Journal of Instruction, 16(3), 551–572. https://doi.org/10.29333/IJI.2023.16330A
Awaliah, S. M., & Sya, M. F. (2025). Implementasi AI Untuk Meningkatkan Keterampilan Membaca (Reading) Bahasa Inggris. Karimah Tauhid, 4(6), 4183–4200. https://doi.org/10.30997/KARIMAHTAUHID.V4I6.19623
Cheng, G., Chwo, G. S. M., & Ng, W. S. (2023). Automated tracking of student revisions in response to teacher feedback in EFL writing: technological feasibility and teachers’ perspectives. Interactive Learning Environments, 31(8), 5236–5260. https://doi.org/10.1080/10494820.2021.2001539
Cosby, A., Fogarty, E. S., & Manning, J. (2023). Digital Literacy and Digital Self-Efficacy of Australian Technology Teachers. Education Sciences 2023, Vol. 13, Page 530, 13(5), 530. https://doi.org/10.3390/EDUCSCI13050530
Cui, Y., & Zhang, H. (2025). Can student accurately identify artificial intelligence generated content? an exploration of AIGC credibility from user perspective in education. Education and Information Technologies 2025 30:12, 30(12), 16321–16346. https://doi.org/10.1007/S10639-025-13448-1
Izah, S. C., Sylva, L., & Hait, M. (2024). Cronbach’s Alpha: A Cornerstone in Ensuring Reliability and Validity in Environmental Health Assessment. ES Energy and Environment, 23. https://doi.org/10.30919/ESEE1057
Jeon, J., & Kim, S. (2022). The mediating effects of digital literacy and self-efficacy on the relationship between learning attitudes and Ehealth literacy in nursing students: A cross-sectional study. Nurse Education Today, 113, 105378. https://doi.org/10.1016/J.NEDT.2022.105378
Katsarou, E. (2021). The Effects of Computer Anxiety and Self-Efficacy on L2 Learners’ Self-Perceived Digital Competence and Satisfaction in Higher Education. Journal of Education and E-Learning Research, 8(2), 158–172. https://doi.org/10.20448/journal.509.2021.82.158.172
Mahasiswa, P., Umpan, T., Dosen, B., Pada, P., Laporan, P., Zulmy, S., Putera, F., & Shofiah, N. (2023). Persepsi Mahasiswa Terhadap Umpan Balik Dosen Pembimbing pada Penyusunan Laporan Skripsi. Jurnal Pendidikan Bahasa Dan Sastra Indonesia Metalingua, 8(2), 86–94. https://doi.org/10.21107/METALINGUA.V8I2.19629
Nazli, K., Jumani, S., & Masum, R. (2025). The Effectiveness of AI-Powered Corrective Feedback on Students’ Writing Performance. Pakistan Social Sciences Review, 9(1), 391–403. https://doi.org/10.35484/PSSR.2025(9-I)31
Park, J. (2019). An AI-based English Grammar Checker vs. Human Raters in Evaluating EFL Learners’ Writing. Multimedia-Assisted Language Learning, 22(1), 112–131.
Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G., & Hanson, J. (2016). Attitude, digital literacy and self efficacy: Flow-on effects for online learning behavior. The Internet and Higher Education, 29, 91–97. https://doi.org/10.1016/J.IHEDUC.2016.01.001
Ranalli, J. (2018). Automated written corrective feedback: how well can students make use of it? Computer Assisted Language Learning, 31(7), 653–674. https://doi.org/10.1080/09588221.2018.1428994
Salsabilla Putri Wijaya, C., Bianca, F., & Sesy Amelia, M. (2025). Artificial Intelligence Sebagai Pendukung Efektifitas dalam Pembelajaran Bahasa Asing. JATI (Jurnal Mahasiswa Teknik Informatika), 9(1), 699–705. https://doi.org/10.36040/JATI.V9I1.12461
Sayaf, A. M., Alamri, M. M., Alqahtani, M. A., & Alrahmi, W. M. (2022). Factors Influencing University Students’ Adoption of Digital Learning Technology in Teaching and Learning. Sustainability 2022, Vol. 14, Page 493, 14(1), 493. https://doi.org/10.3390/SU14010493
Setiawan Uin, A., Thaha, S., Jambi, S., Uin, S. J., Uin, R., & Artikel, I. (2025). Penelitian Metode Campuran (Mixed Method). Arus Jurnal Sosial Dan Humaniora, 5(2), 1484–1491. https://doi.org/10.57250/AJSH.V5I2.1263
Shadiev, R., & Feng, Y. (2024). Using automated corrective feedback tools in language learning: a review study. Interactive Learning Environments, 32(6), 2538–2566. https://doi.org/10.1080/10494820.2022.2153145
Staddon, R. V. (2023). Exploring Higher Education Students’ Perspectives on Factors Affecting Use, Attitudes and Confidence with Learning Technologies. International Journal of Instruction, 16(2), 31–52. https://e-iji.net/ats/index.php/pub/article/view/129
Williams, C., & Beam, S. (2019). Technology and writing: Review of research. Computers & Education, 128, 227–242. https://doi.org/10.1016/J.COMPEDU.2018.09.024
Yama P. Sumbodo, S. I. M. I. yama, Marzuki, S. Sos. , M. Pd. , M., apt. Sandi Mahesa Yudhantara, M. Farm. , sandi, & Dr. Widiastuti, SS. , M. Hum. , widiastuti. (2024). Metode penelitian panduan lengkap untuk penelitian kuantitatif, kualitatif, dan campuran. Metode Penelitian Kualitatif, 1.
Zhang, Z., & Hyland, K. (2025). The role of digital literacy in student engagement with automated writing evaluation (AWE) feedback on second language writing. Computer Assisted Language Learning, 38(5–6), 1060–1085. https://doi.org/10.1080/09588221.2023.2256815
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