Monitoring AI Use in Language Classes: A Phenomenological Study of English Teachers' Strategies to Evaluate AI-Assisted Student Submissions

المؤلفون

  • Jean Domalanta St. Louis University, Baguio City
  • Nan Hkawn Nding
  • Htu Pan Nbwi
  • Hein Thuzar Naing
  • Jiamin Guo
  • Maehaila Nicole Mendioro

DOI:

https://doi.org/10.31436/ijes.v14i1.642

الكلمات المفتاحية:

Artificial Intelligence tools، Grammarly، English teachers، academic integrity، monitoring strategies، AI-assisted student submissions، interpretative phenomenological analysis

الملخص

This study explores how English teachers in Myanmar’s community colleges monitor and evaluate AI-assisted student submissions. With the rise of artificial intelligence tools such as Grammarly and GPT-2, educators encounter challenges in ensuring academic integrity and fostering critical thinking. Drawing on interpretative phenomenological analysis, the research investigates the lived experiences of six English teachers with one to six years of teaching experience. The study uses in-depth interviews to examine teachers’ strategies for detecting AI-generated content and ensuring originality in student work. The findings show that, despite recognizing the inevitability of AI’s integration into academia, the teachers emphasized the importance of empowering students and using personal evaluation methods, such as providing constructive feedback and employing rubrics, to maintain academic integrity. While AI tools offer valuable educational support, balancing innovative teaching with ethical considerations is crucial. This research provides insights for educators, administrators, and policymakers to develop strategies and tools that foster academic honesty and support the responsible use of AI in educational contexts.

المقاييس

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التنزيلات

منشور

2026-01-31

كيفية الاقتباس

Domalanta, J., Nding, N. H., Nbwi, H. P. ., Naing, H. T., Guo, J., & Mendioro, M. N. . (2026). Monitoring AI Use in Language Classes: A Phenomenological Study of English Teachers’ Strategies to Evaluate AI-Assisted Student Submissions. IIUM Journal of Educational Studies, 14(1), 104–124. https://doi.org/10.31436/ijes.v14i1.642
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