%0 Journal Article
%T Study on the Improvement of Learners¡¯ Academic Writing Competence Based on Multi-Round Automatic Feedback Supported by Generative Artificial Intelligence
%A Liang Chen
%J Open Access Library Journal
%V 12
%N 12
%P 1-14
%@ 2333-9721
%D 2025
%I Open Access Library
%R 10.4236/oalib.1114645
%X This study explores the empowerment mechanisms and optimization paths of Generative Artificial Intelligence (AIGC) for academic writing competence. By constructing a human-machine collaboration framework covering the entire writing cycle, it proposes a four-dimensional interactive model of ¡°Delay-Dialogue-Practice-Evaluation¡±: in the pre-writing phase, a three-stage cognitive intervention mechanism is adopted to avoid technology dependence; in the text generation phase, a hierarchical creation model and a Socratic dialogue system are developed; in the evaluation phase, a multi-dimensional diagnosis and metacognitive reflection mechanism is established. This study innovatively integrates process writing theory with the characteristics of AI technology, providing a sustainable development plan for academic writing education in the intelligent era that balances efficiency improvement and ethical constraints.
%K AIGC
%K Academic Writing
%K Framework Design
%K Learners
%K Multi-Round Feedback
%U http://www.oalib.com/paper/6881121