AI-Driven Corrective Feedback for Low-Proficiency Learners: A Study on Writing Skill Development
16
Issued Date
2025-01-01
Resource Type
ISSN
18650929
eISSN
18650937
Scopus ID
2-s2.0-105013022647
Journal Title
Communications in Computer and Information Science
Volume
2551 CCIS
Start Page
3
End Page
20
Rights Holder(s)
SCOPUS
Bibliographic Citation
Communications in Computer and Information Science Vol.2551 CCIS (2025) , 3-20
Suggested Citation
Wiboolyasarin W., Kiti T., Wiboolyasarin K., Tiranant P., Jinowat N. AI-Driven Corrective Feedback for Low-Proficiency Learners: A Study on Writing Skill Development. Communications in Computer and Information Science Vol.2551 CCIS (2025) , 3-20. 20. doi:10.1007/978-3-031-98003-9_1 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111725
Title
AI-Driven Corrective Feedback for Low-Proficiency Learners: A Study on Writing Skill Development
Corresponding Author(s)
Other Contributor(s)
Abstract
Effective writing is a critical skill in second language (L2) acquisition, yet low-proficiency learners often face significant challenges, which impede their progress and confidence. This study investigates the impact of AI-driven corrective feedback, specifically utilizing ChatGPT, on improving the writing skills of low-proficiency learners. By addressing challenges such as grammatical inaccuracies, limited vocabulary, and difficulties in structuring coherent texts, AI-driven feedback tools offer scalable, personalized solutions to enhance second language writing instruction. A quasi-experimental design was employed with 19 participants engaged in a 10-week intervention featuring iterative writing tasks and real-time feedback from ChatGPT. The results revealed statistically significant improvements across all five assessed components of writing: grammar, vocabulary, sentence structure, coherence, and task achievement. Sentence structure and coherence exhibited the most substantial gains, highlighting the tool’s ability to help learners construct complex sentences and logically organize ideas. The study underscores the transformative potential of AI-driven feedback to support iterative learning processes, reduce teacher workload, and foster autonomy among low-proficiency learners. These findings contribute valuable insights for educators, researchers, and developers aiming to integrate AI technologies into effective language learning frameworks.