EFL LEARNERS VIEWS ON AI FEEDBACK TOOLS: ASSESSING GRAMMAR ACCURACY AND LEARNING IMPACT

  • Meyga Agustia Nindya Universitas Pancasakti Tegal
  • Taufiqulloh Taufiqulloh Universitas Pancasakti Tegal
DOI: https://doi.org/10.25134/erjee.v12i3.10672
Keywords: EFL Learners, Grammar Accuracy, AI Feedback Tools, Self-Assessment, Language Learning

Abstract

This study explores English as a Foreign Language (EFL) learners' perceptions of grammar accuracy and the impact of AI-driven feedback tools on their self-assessment and improvement. Utilizing a mixed-methods approach, data were collected from 54 EFL students majoring in English Language Teaching at Universitas Pancasakti Tegal through quantitative surveys. The findings indicate that a significant majority of participants perceive AI feedback as accurate (57.4% finding it accurate; 29.6% very accurate), useful (53.7% rating it helpful), and satisfying (66.7% satisfied). Furthermore, learners reported that AI tools effectively identify missed errors and enhance their understanding of grammar rules. Despite the positive perceptions, a portion of participants still values traditional feedback, suggesting a potential benefit in a blended approach that combines AI and human interaction. This research underscores the growing importance of integrating AI-driven tools in language education, highlighting their role in enhancing learner engagement and grammar proficiency. Future studies should investigate the long-term effects of AI feedback on language learning outcomes and explore specific features that maximize its effectiveness.

References

Andrade, H. G., & Valtcheva, A. (2009). Promoting learning and achievement through self-assessment. Theory Into Practice, 48(1), 12-19. https://doi.org/10.1080/00405840802577544

Baker, R. S., & Inventado, P. S. (2019). Educational data mining and learning analytics: A survey of the fields. Computers & Education, 113, 212–229. https://doi.org/10.1016/j.compedu.2017.06.003

Bitchener, J., & Knoch, U. (2008). The contribution of written corrective feedback to language development: A review of the research. Journal of Second Language Writing, 17(4), 207-217. https://doi.org/10.1016/j.jslw.2008.06.004

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101. https://doi.org/10.1191/1478088706qp063oa

Chen, X., Lin, H., & Xu, H. (2024). Exploring the long-term effects of AI-driven feedback tools on learner autonomy and grammar accuracy. Language Learning & Technology, 28(2), 34-52. https://doi.org/10.1016/j.langlt.2024.01.002

Chiu, T. K., & Tsai, H. M. (2020). Exploring the relationship between feedback and student engagement in blended learning. Computers & Education, 152, Article 103872. https://doi.org/10.1016/j.compedu.2020.103872

Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education. Routledge.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. SAGE Publications.

Denscombe, M. (2014). The good research guide: For small-scale social research projects. Open University Press.

Dörnyei, Z. (2021). Motivational strategies in the language classroom. Cambridge University Press. https://doi.org/10.1017/9781108550205

Ellis, R. (2008). The study of second language acquisition. Oxford University Press.

Ferris, D. R. (1999). The case for written corrective feedback. University of Michigan Press.

Fontana, A., & Frey, J. H. (2005). The interview: From structured questions to negotiated text. In N. K. Denzin & Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research (pp. 695-727). SAGE Publications.

Graham, S. (2013). The role of writing in academic learning. In J. Hattie & E. Anderman (Eds.), Handbook of student engagement (pp. 99–115). Academic Press. https://doi.org/10.1016/B978-0-12-369424-3.00007-1

Hattie, J., & Donoghue, G. (2016). Learning strategies: A synthesis and conceptual framework. Educational Psychologist, 51(2), 153–169. https://doi.org/10.1080/00461520.2016.1143620

Hattie, J., & Timperley, H. (2017). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/0034654319898402

Hegelheimer, V., & Lee, L. (2020). Evaluating the effectiveness of automated feedback tools in language learning. CALICO Journal, 37(1), 58-73. https://doi.org/10.1558/cj.37345

Hsu, T. (2020). Artificial intelligence in language education: Current applications and future prospects. Journal of Educational Technology, 18(3), 45-62. https://doi.org/10.1007/s11528-020-00509-2

Huang, Y. M., & Chang, C. Y. (2020). The role of technology in enhancing student engagement: A framework for assessing the influence of AI in education. Computers in Human Behavior, 113, Article 106505. https://doi.org/10.1016/j.chb.2020.106505

Kormos, J., & Trebits, A. (2012). The impact of feedback on the accuracy of language production: The case of written corrective feedback. Language Teaching Research, 16(3), 369-390. https://doi.org/10.1177/1362168812439617

Kukulska-Hulme, A. (2021). The role of AI in language learning: Potential and challenges. Computer Assisted Language Learning, 34(6), 675-693. https://doi.org/10.1080/09588221.2020.1829601

Lee, L., & Hsu, T. (2018). The integration of AI tools in language learning: A comparative study of effectiveness and learner satisfaction. Language Learning & Technology, 22(3), 1-18. https://doi.org/10.1007/s11423-018-9530-7

Lee, I. (2017). Feedback in L2 writing: A review of the literature. Language Teaching, 50(3), 262–284. https://doi.org/10.1017/S0261444817000229

Lightbown, P. M., & Spada, N. (2013). How languages are learned. Oxford University Press.

Liu, N., & Carless, D. (2006). Peer feedback: The role of learner reflection in developing language accuracy. Language Teaching Research, 10(3), 296-314. https://doi.org/10.1191/1362168806lr201oa

Liu, X., Zhang, W., & Zhang, C. (2022). Comparative analysis of AI and human feedback on language learning outcomes. Language Teaching Research, 26(4), 525-545. https://doi.org/10.1177/136

Lyster, R., & Saito, H. (2010). Oral feedback in second language classrooms. Language Teaching Research, 14(3), 391-418. https://doi.org/10.1177/1362168810375367

Mason, M. (2018). Qualitative research methods: A data collector's field guide. Routledge.

McMillan, J. H., & Hearn, J. (2008). Student self-assessment: What do we know? Practical Assessment, Research & Evaluation, 14(1), 1-11. https://doi.org/10.7275/hh63-r788

Nicol, D. J. (2020). Transforming assessment and feedback: Improving student learning through the use of formative assessment. The University of Edinburgh. https://doi.org/10.1007/s10798-020-09538-4

Norris, J. M., & Ortega, L. (2000). Effectiveness of L2 instruction: A research synthesis and quantitative meta-analysis. Language Learning, 50(3), 417-528. https://doi.org/10.1111/0023-8333.00136

Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Allen & Unwin.

Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice. SAGE Publications.

Saito, H., & Lyster, R. (2012). The role of corrective feedback in second language acquisition. Language Teaching Research, 16(3), 1-20. https://doi.org/10.1177/1362168812446331

Shen, Z., Lu, X., & Liu, Y. (2022). Advances in Natural Language Processing for Educational Technology. International Journal of Artificial Intelligence in Education, 32(4), 489-505.

Sheen, Y. (2007). The effect of corrective feedback and language anxiety on L2 learners’ grammar acquisition. Language Teaching Research, 11(2), 165-184.https://doi.org/10.1177/1362168807075003

Spada, N. (2011). Instructional effectiveness in second language acquisition. Language Teaching Research, 15(3), 305-331. https://doi.org/10.1177/1362168811400974

Sweller, J. (2019). Cognitive load theory: A framework for understanding the relationship between learning and instruction. Educational Psychology Review, 31(2), 325–332. https://doi.org/10.1007/s10648-018-9441-2

Tseng, W. T. (2021). The impact of AI feedback on EFL learners' writing skills: A meta-analysis. Educational Technology & Society, 24(3), 10–24. https://www.jstor.org/stable/26770012

VanPatten, B., & Williams, J. (2015). Theories in second language acquisition: An introduction. Routledge.

Varnhagen, C. K., McCallum, T. J., & McGowan, S. (2017). The effectiveness of automated versus human feedback in writing instruction. Journal of Educational Technology & Society, 20(2), 78-91. https://www.jstor.org/stable/23601387

Yoon, H. J., & Polio, C. (2017). The effectiveness of automated feedback on second language learners’ grammatical accuracy. Language Learning & Technology, 21(3), 1-17. https://doi.org/10.1016/j.langlt.2017.04.004

Zhao, Y. (2023). Learners' attitudes towards AI feedback in language learning: A review. Educational Technology Research and Development, 71(2), 123-145. https://doi.org/10.1007/s11423-022-10134-5

Zou, D. (2022). Integrating AI tools into language learning: Challenges and opportunities. Journal of Language and Linguistics, 19(4), 88-102. https://doi.org/10.1016/j.jll.2022.07.004

Published
2024-10-10
How to Cite
Nindya, M. A., & Taufiqulloh, T. (2024). EFL LEARNERS VIEWS ON AI FEEDBACK TOOLS: ASSESSING GRAMMAR ACCURACY AND LEARNING IMPACT. English Review: Journal of English Education, 12(3), 993-1002. https://doi.org/10.25134/erjee.v12i3.10672
Issue
Vol. 12 No. 3 (2024)
Section
Articles

All articles published in English Review: Journal of English Education (ERJEE) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

 

Copyright Ownership

Authors retain the copyright of their articles and grant ERJEE the right of first publication. The journal is granted a non-exclusive license to publish, reproduce, and distribute the article in any format, medium, or platform, provided that proper credit is given to the original authors.

 

License Terms – CC BY 4.0

Under the Creative Commons Attribution 4.0 International License, others are free to:

  • Share — copy and redistribute the material in any medium or format
  • Adapt — remix, transform, and build upon the material for any purpose, even commercially

As long as they:

  • Provide appropriate credit to the original author(s) and source
  • Provide a link to the license (https://creativecommons.org/licenses/by/4.0/)
  • Indicate if any changes were made

There are no restrictions on the reuse, reproduction, or adaptation of published articles as long as attribution is properly given.

 

Author Warranties

By submitting a manuscript to ERJEE, authors confirm that:

  • The work is original and does not infringe any existing copyright.
  • The manuscript has not been previously published and is not under consideration elsewhere.
  • All sources and references are appropriately acknowledged.
  • Necessary permissions have been obtained for any copyrighted materials used.