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Ding, J.H., Fan, Z.H. and Liu, H.Z. (2024) Visualization and Correlation Analysis of Learning Engagement Based on Multimodal Data of Collaborative Programming—Understanding the Interaction Between Behavior, Cognition, Society, and Emotion and Its Impact on Learning. Journal of Distance Education, 42, 40-49.

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• TITLE: Study on the Improvement of Learners’ Academic Writing Competence Based on Multi-Round Automatic Feedback Supported by Generative Artificial Intelligence

  AUTHORS: Liang Chen

  KEYWORDS: AIGC, Academic Writing, Framework Design, Learners, Multi-Round Feedback

  JOURNAL NAME: Open Access Library Journal, Vol.12 No.12, December 23, 2025

  ABSTRACT: 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.