Skip to main content
Suluh Pendidikan · Jurnal Ilmu-Ilmu Pendidikan
Suluh PendidikanIKIP Saraswati
Vol. 23 No. 2 (2025) / Articles

AI-DRIVEN FEEDBACK FOR VOCABULARY DEVELOPMENT: A BEFORE-AFTER ANALYSIS IN INDONESIAN TERTIARY EFL WRITING

Ni Ines Marylena Candra Manik (UHN I Gusti Bagus Sugriwa Denpasar)
Putu Wiraningsih ( UHN I Gusti Bagus Sugriwa Denpasar)
Agus Budiarte ( UHN I Gusti Bagus Sugriwa Denpasar)
DOI: 10.46444/suluh-pendidikan.v23i2.868 Published: 22 December 2025
Vol. 23 No. 2 (2025): SULUH PENDIDIKAN : Jurnal Ilmu-Ilmu Pendidikan

Abstract

This study aims to analyze the effectiveness of AI-driven feedback in enhancing vocabulary development among Indonesian tertiary EFL (English as a Foreign Language) students. Using a before-after research design, the study involved 60 undergraduate English majors from a public university in Indonesia. Participants were asked to write two academic essays—before and after receiving automated feedback from AI-based platforms such as Grammarly and ChatGPT. The analysis focused on improvements in lexical complexity, vocabulary range, and appropriate collocation use. Quantitative data were examined through paired-sample t-tests, supported by qualitative analysis of the most influential feedback types contributing to lexical improvement. The findings revealed a significant increase in students’ vocabulary range and depth after the application of AI-driven feedback, with a significance level of p < 0.05. Moreover, students reported that AI feedback offered more personalized, immediate, and exploratory learning compared to manual correction from lecturers. However, the study also identified limitations related to students’ overreliance on automated suggestions without deeper linguistic understanding. The results suggest that integrating AI-driven feedback into EFL academic writing can serve as an effective strategy to foster lexical competence, provided it is complemented by appropriate pedagogical guidance from instructors.

References

  1. Afifah, N. Y., & Dewi, U. (2025). Students’ Perception of Using Paperpal AI as An Automated Writing Evaluation (Awe) Tool on Writing Grammatical Feedback. FRASA: English Education and Literature Journal, 6(2), 131. https://doi.org/10.47701/frasa.v6i2.5009
  2. Astutik, I., Widiati, U., Ratri, D. P., Jonathans, P. M., Nurkamilah, N., Devanti, Y. M., & Harfal, Z. (2024). Transformative Practices: Integrating Automated Writing Evaluation in Higher Education Writing Classrooms - A Systematic Review. Indonesian Journal on Learning and Advanced Education (IJOLAE), 423–441. https://doi.org/10.23917/ijolae.v6i3.23675
  3. Baskara, F. R. (2023). Integrating ChatGPT into EFL writing instruction: Benefits and challenges. International Journal of Education and Learning, 5(1), 44–55. https://doi.org/10.31763/ijele.v5i1.858
  4. Dewi, U. (2023). Grammarly as Automated Writing Evaluation: Its Effectiveness from EFL Students’ Perceptions. Lingua Cultura, 16(2), 155–161. https://doi.org/10.21512/lc.v16i2.8315
  5. Lestari, E., & Rokhayati, T. (2025). Enhancing Students’ Writing Skills: The Impact, Engagement, and Optimization of Automated and Teacher Written Corrective Feedback. Jurnal Pendidikan Bahasa Inggris Undiksha, 13(2), 196–207. https://doi.org/10.23887/jpbi.v13i2.92886
  6. Miranty, D., Widiati, U., Cahyono, B. Y., & Sharif, T. I. S. T. (2023). Automated writing evaluation tools for Indonesian undergraduate English as a foreign language students’ writing. International Journal of Evaluation and Research in Education, 12(3), 1705–1715. https://doi.org/10.11591/ijere.v12i3.24958
  7. Nguyen, T. T. H. (2023). EFL Teachers’ Perspectives toward the Use of ChatGPT in Writing Classes: A Case Study at Van Lang University. International Journal of Language Instruction, 2(3), 1–47. https://doi.org/10.54855/ijli.23231
  8. Petchprasert, A. (2021). Utilizing an automated tool analysis to evaluate EFL students’ writing performances. Asian-Pacific Journal of Second and Foreign Language Education, 6(1). https://doi.org/10.1186/s40862-020-00107-w
  9. Rahman, N. A., & Rizqy, P. U. (2024). Bridging the Gap in Lifelong Learning: Policy, Technology, and Equity in Digital Education Access. Sinergi International Journal of Education, 2(2), 114–129. https://journal.sinergi.or.id/index.php/education
  10. Saudin, Sulyaningsih, I., & Meilinda, L. (2017). The investigation of productive and receptive competence in v+n and adj+n collocations among Indonesian EFL learners. Indonesian Journal of Applied Linguistics, 7(1), 189–200. https://doi.org/10.17509/ijal.v7i1.6874
  11. Tram, N. H. M., Nguyen, T. T., & Tran, C. D. (2024). ChatGPT as a tool for self-learning English among EFL learners: A multi-methods study. System, 127. https://doi.org/10.1016/j.system.2024.103528
  12. Wei, P., Wang, X., & Dong, H. (2023). The impact of automated writing evaluation on second language writing skills of Chinese EFL learners: a randomized controlled trial. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1249991

Usage Statistics

Metrics & Statistics

385 Views 132 Downloads pp. 205-210
PDF

Keywords

Artificial Intelligence Automated Feedback Vocabulary Development EFL Learning Academic Writing

sintacertf

  • ACCREDITATION CERTIFICATE

issn

ISSN
ISSN Certificate E-ISSN Certificate

indexing

  • INDEXING

visitcounter

  • STATISTIC COUNTER

    Flag Counter

citation

  • CITATION

lokasi

  • PETA LOKASI

Suluh Pendidikan : Jurnal Ilmu-Ilmu Pendidikan

Similar Articles