A LLM-based pedagogical framework for active, inquiry-based and adaptive learning in L2 writing
Traditional L2 writing instruction often struggles to provide personalized, process-oriented feedback and engage student motivation. While generative AI like ChatGPT offers a potential solution, its application lacks a robust pedagogical foundation. This study proposes an innovative framework that integrates ChatGPT into L2 writing through a synthesis of active, inquiry-based, and adaptive learning principles. Within the framework, learners occupy the central position, undergoing the teaching implementation of LLM-based six-step writing instruction process to inquisitively learn, experience the teaching assessment of three-dimensional writing evaluation to actively acquire, and the teaching reflection of the plan for the subsequent writing instruction to adaptively improve. Based on a quasi-experimental study involving 50 sophomores, the framework is proved to be effective, enhancing significantly learners’ writing outcomes and motivation. These findings add to the limited body of research on the utilization of ChatGPT in education, providing valuable implications for research and pedagogical practices in L2 writing.
Authors
- Yan Yin (ORCID: https://orcid.org/0000-0003-1655-6831)
- Ruonan Wang
- Yongbo Cao
Institutions
- Hubei University (CN)
- Wuhan Institute of Technology (CN)
Publication Details
- Journal
- Computers and Education Artificial Intelligence
- Published
- 2025-12-20
- DOI
- https://doi.org/10.1016/j.caeai.2025.100535
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00