Research Article
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AI-assisted grammar learning: Improving present perfect tense proficiency in EFL students

Year 2024, Volume: 10 Issue: 3, 339 - 361, 31.12.2024
https://doi.org/10.47216/literacytrek.1556837

Abstract

This study explores how artificial intelligence (AI)-assisted feedback mechanisms influence self-regulated learning (SRL) processes and grammatical accuracy among Turkish EFL learners focusing on the Present Perfect Tense.Conducted with 18 Turkish university preparatory students, this qualitative research employed pre- and post-writing tasks, interactive sessions with an AI chatbot, and an open-ended survey to explore learners’ experiences. The findings reveal that 72% of participants demonstrated improved grammatical accuracy, while many reported enhanced autonomy and confidence. The study highlights the role of AI chatbots in fostering SRL behaviours such as goal-setting, self-monitoring, and reflection through immediate and adaptive feedback mechanisms. By addressing a critical gap in EFL grammar instruction, this research contributes to the growing evidence of AI's potential to personalise learning and support the acquisition of complex grammatical structures. Despite the study's promising outcomes, limitations such as the small sample size and lack of long-term assessments are noted. Future research should focus on larger and more diverse samples, longitudinal evaluations, and advanced AI features to further enhance language learning experiences.

Ethical Statement

This research study was conducted with the Research Ethics Committee approval of MEF University, dated 19.10.2023 and numbered E-47749665-050.01.04-3987.

Supporting Institution

N/A

Project Number

N/A

Thanks

MEF UNIVERSITY, FACULTY OF EDUCATION

References

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  • Chen, H., Wang, T., & Lin, C. (2023). Mobile-assisted language learning: Grammar improvement in Chinese EFL students. Journal of Educational Technology & Society, 24(2), 34-45.
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  • Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage.
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  • Dai, K., &. Liu, Q. (2024). Leveraging artificial intelligence (AI) in English as a foreign language (EFL) classes: Challenges and opportunities in the spotlight. Computers in Human Behavior, 159, 108354
  • Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340.
  • Denzin, N. K. (2006). Sociological methods: A sourcebook (5th ed.). Aldine Transaction.
  • Ellis, R., & Barkhuizen, G. (2020). Analysing learner language. Oxford University Press.
  • Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87-105.
  • Hopkins, L. (2021). AI and personalised learning in language education. Technology and Language, 3(2), 34–52.
  • Jamal, A. (2023). The role of artificial intelligence (AI) in teacher education: Opportunities and challenges. International Journal of Research and Analytical Reviews, 10(1), 139–146.
  • Jiang, R. (2022). How does artificial intelligence empower EFL teaching and learning nowadays? A review on artificial intelligence in the EFL context. Frontiers in Psychology,13. 1-8.
  • Kim, M. (2019). The impact of AI chatbots on grammar learning: A study of EFL learners. Journal of Language and Artificial Intelligence, 8(1), 45–62.
  • Lin, C., Wang, Y., & Chen, X. (2020). Game-based learning and contextual simulations: Enhancing grammar acquisition in EFL students. Journal of Computer-Assisted Language Learning, 33(3), 101–120.
  • Mahmoodi, M. H., Kalantari, B., & Ghaslani, R. (2014). Self-regulated learning (SRL), motivation, and language achievement of Iranian EFL learners. Procedia - Social and Behavioral Sciences, 98, 1062–1068.
  • Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., & Fleckenstein, J. (2024). Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions. Computers and Education: Artificial Intelligence, 6, 100199.
  • Na, Y.-H., Ahn, B.-K., & Kim, H.-S. (2008). Evaluating an in-service English teacher training program from multiple perspectives. English Teaching, 63(4), 273–302.
  • Oliver-Hoyo, M., & Allen, D. (2006). The use of triangulation methods in qualitative educational research. Journal of College Science Teaching, 35(4), 42–47.
  • Oxford, R. L. (2001). Language learning strategies. In R. Carter & D. Nunan (Eds.), The Cambridge guide to teaching English to speakers of other languages (pp. 166–172). Cambridge University Press.
  • Pintrich, P. R. (2005). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 45(3),199-205.
  • Ranjbari, M. N., Tabrizi, H. H., & Afghari, A. (2020). Evaluation of the latest pre-service teacher education curriculum in EFL context: A testimony of teachers, teacher educators, and student teachers' perspectives. Applied Research on English Language, 9(1), 1–24.
  • Ruwe, T., & Mayweg-Paus, E. (2023). “Your argumentation is good,” says the AI vs. humans – The role of feedback providers and personalised language for feedback effectiveness. Computers and Education: Artificial Intelligence, 5, 100189.
  • Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6, 100169.
  • Warschauer, M. (2002). Reconceptualising the digital divide.First Monday, 7(1).Available at https://firstmonday.org/ojs/index.php/fm/art
  • Wang, T., Chen, H., & Lin, C. (2021). Mobile-assisted language learning: Grammar improvement in Chinese EFL students. Journal of Educational Technology & Society, 24(2), 34–45.
  • Yang, A. (2024). Challenges and opportunities for foreign language teachers in the era of artificial intelligence. International Journal of Education and Humanities (IJEH), 4(1), 1-12.
  • Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70.
Year 2024, Volume: 10 Issue: 3, 339 - 361, 31.12.2024
https://doi.org/10.47216/literacytrek.1556837

Abstract

Project Number

N/A

References

  • Bikowski, D. (2018). Technology for teaching grammar. In J. I. Liontas (Ed.), The TESOL Encyclopedia of English Language Teaching (pp. 1-7). Wiley
  • Chen, H., Wang, T., & Lin, C. (2023). Mobile-assisted language learning: Grammar improvement in Chinese EFL students. Journal of Educational Technology & Society, 24(2), 34-45.
  • Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing among five approaches (2nd ed.). Sage.
  • Creswell, J. W., & Poth, C. N. (2017). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage.
  • Crovitz, D., Devereaux, M. D., & Moran, C. M. (2022). Next level grammar for a digital age: Teaching with social media and online tools for rhetorical understanding and critical creation. Routledge.
  • Council of Europe. (2001). Common European Framework of Reference for Languages: Learning, teaching, assessment. Cambridge University Press.
  • Dai, K., &. Liu, Q. (2024). Leveraging artificial intelligence (AI) in English as a foreign language (EFL) classes: Challenges and opportunities in the spotlight. Computers in Human Behavior, 159, 108354
  • Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340.
  • Denzin, N. K. (2006). Sociological methods: A sourcebook (5th ed.). Aldine Transaction.
  • Ellis, R., & Barkhuizen, G. (2020). Analysing learner language. Oxford University Press.
  • Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87-105.
  • Hopkins, L. (2021). AI and personalised learning in language education. Technology and Language, 3(2), 34–52.
  • Jamal, A. (2023). The role of artificial intelligence (AI) in teacher education: Opportunities and challenges. International Journal of Research and Analytical Reviews, 10(1), 139–146.
  • Jiang, R. (2022). How does artificial intelligence empower EFL teaching and learning nowadays? A review on artificial intelligence in the EFL context. Frontiers in Psychology,13. 1-8.
  • Kim, M. (2019). The impact of AI chatbots on grammar learning: A study of EFL learners. Journal of Language and Artificial Intelligence, 8(1), 45–62.
  • Lin, C., Wang, Y., & Chen, X. (2020). Game-based learning and contextual simulations: Enhancing grammar acquisition in EFL students. Journal of Computer-Assisted Language Learning, 33(3), 101–120.
  • Mahmoodi, M. H., Kalantari, B., & Ghaslani, R. (2014). Self-regulated learning (SRL), motivation, and language achievement of Iranian EFL learners. Procedia - Social and Behavioral Sciences, 98, 1062–1068.
  • Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., & Fleckenstein, J. (2024). Using LLMs to bring evidence-based feedback into the classroom: AI-generated feedback increases secondary students’ text revision, motivation, and positive emotions. Computers and Education: Artificial Intelligence, 6, 100199.
  • Na, Y.-H., Ahn, B.-K., & Kim, H.-S. (2008). Evaluating an in-service English teacher training program from multiple perspectives. English Teaching, 63(4), 273–302.
  • Oliver-Hoyo, M., & Allen, D. (2006). The use of triangulation methods in qualitative educational research. Journal of College Science Teaching, 35(4), 42–47.
  • Oxford, R. L. (2001). Language learning strategies. In R. Carter & D. Nunan (Eds.), The Cambridge guide to teaching English to speakers of other languages (pp. 166–172). Cambridge University Press.
  • Pintrich, P. R. (2005). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 45(3),199-205.
  • Ranjbari, M. N., Tabrizi, H. H., & Afghari, A. (2020). Evaluation of the latest pre-service teacher education curriculum in EFL context: A testimony of teachers, teacher educators, and student teachers' perspectives. Applied Research on English Language, 9(1), 1–24.
  • Ruwe, T., & Mayweg-Paus, E. (2023). “Your argumentation is good,” says the AI vs. humans – The role of feedback providers and personalised language for feedback effectiveness. Computers and Education: Artificial Intelligence, 5, 100189.
  • Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6, 100169.
  • Warschauer, M. (2002). Reconceptualising the digital divide.First Monday, 7(1).Available at https://firstmonday.org/ojs/index.php/fm/art
  • Wang, T., Chen, H., & Lin, C. (2021). Mobile-assisted language learning: Grammar improvement in Chinese EFL students. Journal of Educational Technology & Society, 24(2), 34–45.
  • Yang, A. (2024). Challenges and opportunities for foreign language teachers in the era of artificial intelligence. International Journal of Education and Humanities (IJEH), 4(1), 1-12.
  • Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70.
There are 29 citations in total.

Details

Primary Language English
Subjects English As A Second Language, Computational Linguistics
Journal Section Research Articles
Authors

Barkın Yener 0009-0006-8872-0070

Hasan Selcuk 0000-0001-8098-1088

Project Number N/A
Publication Date December 31, 2024
Submission Date September 27, 2024
Acceptance Date December 26, 2024
Published in Issue Year 2024 Volume: 10 Issue: 3

Cite

APA Yener, B., & Selcuk, H. (2024). AI-assisted grammar learning: Improving present perfect tense proficiency in EFL students. The Literacy Trek, 10(3), 339-361. https://doi.org/10.47216/literacytrek.1556837

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