Writing With Machines and Peers: Designing for Critical Engagement with Generative AI

• URL: http://arxiv.org/abs/2511.15750v1
• Date: Wed, 19 Nov 2025 02:17:42 GMT
• Title: Writing With Machines and Peers: Designing for Critical Engagement with Generative AI
• Authors: Xinran Zhu, Cong Wang, Duane Searsmith,
• Abstract summary: This study proposes a pedagogical design that integrates AI and peer feedback in a graduate-level academic writing activity.<n>Students developed literature review projects through multiple writing and revision stages, receiving feedback from both a custom-built AI reviewer and human peers.
• Score: 5.719812010814006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing integration of generative AI in higher education is transforming how students write, learn, and engage with knowledge. As AI tools become more integrated into classrooms, there is an urgent need for pedagogical approaches that help students use them critically and reflectively. This study proposes a pedagogical design that integrates AI and peer feedback in a graduate-level academic writing activity. Over eight weeks, students developed literature review projects through multiple writing and revision stages, receiving feedback from both a custom-built AI reviewer and human peers. We examine two questions: (1) How did students interact with and incorporate AI and peer feedback during the writing process? and (2) How did they reflect on and build relationships with both human and AI reviewers? Data sources include student writing artifacts, AI and peer feedback, AI chat logs, and student reflections. Findings show that students engaged differently with each feedback source-relying on AI for rubric alignment and surface-level edits, and on peer feedback for conceptual development and disciplinary relevance. Reflections revealed evolving relationships with AI, characterized by increasing confidence, strategic use, and critical awareness of its limitations. The pedagogical design supported writing development, AI literacy, and disciplinary understanding. This study offers a scalable pedagogical model for integrating AI into writing instruction and contributes insights for system-level approaches to fostering meaningful human-AI collaboration in higher education.

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