Leveraging the potential of ChatGPT as an automated writing evaluation (AWE) tool: Students' feedback literacy development and AWE tools integration framework




Section: Regular Articles


DOI: https://doi.org/10.29140/jaltcall.v20n1.1200


This study examines the use of ChatGPT, in conjunction with other applications, Grammarly and Quillbot, as an Automated Writing Evaluation (AWE) tool in a Recount and Narrative essay course for 18 English as a Foreign Language (EFL) students in Indonesia. The primary objective was to investigate the impact of these tools on the development of learners' feedback literacy. A single, qualitative case study design was employed, gathering students' voices through a semi-structured interview, reflective journals, and artifacts comprising writing e-portfolios and the feedback evidence. The data analysis utilized an inductive-deductive approach to identify themes and patterns in the qualitative data. The findings revealed that the AWE tools complemented each other in supporting almost all aspects of students' feedback literacy, with "feedback processing" being the aspect that ChatGPT could potentially enhance or diminish, contingent upon students' feedback-seeking behavior. Furthermore, as the result of the inductive coding of the qualitative data, the study offers an "AWE Tools Integration Framework," namely six elements that educators could consider when incorporating AWE tools, notably the generative AI, in their writing classes. This study concluded with a call for greater support of students' digital literacy, equal access to technology, and ethical use of Artificial Intelligence in the classroom.


Keywords: Artificial Intelligence, assessment, Automated Writing Evaluation (AWE) tools, feedback literacy

Suggested Citation:

Gozali, I., Wijaya, A. R. T., Lie, A., Cahyono, B. Y., & Suryati, N. (2024). Leveraging the potential of ChatGPT as an automated writing evaluation (AWE) tool: Students’ feedback literacy development and AWE tools integration framework. The JALT CALL Journal, 20(1), 1–22. https://doi.org/10.29140/jaltcall.v20n1.1200


Metrics Loading ...