Kirsti Cole, Biven Alexander, Wil Carr, Brody McCurdy, and Bethany Van Scooter
North Carolina State University
This collaboratively developed assignment engages students in designing and practicing ethical, multimodal feedback within digital and GenAI-assisted writing contexts. Over three scaffolded weeks, students co-create feedback criteria and rubrics, experiment with giving and receiving feedback across formats (written, audio, screencast), and critically evaluate AI-generated feedback. Designed to promote transparency, reflection, and agency in writing assessment, this assignment encourages students to think deeply about labor, authorship, and equity, which are key concerns in an era of generative AI and digital composing.
Learning Goals
Original Assignment Context: This assignment was developed for CRDM 704: Communication, Technologies, and Pedagogy, a graduate seminar at North Carolina State University focused on digital writing, instructional design, and critical engagement with emerging technologies. It was originally piloted in an upper-level undergraduate writing course where students had prior experience with peer review and multimodal composition. In other contexts, such as first-year writing or professional communication courses, this assignment can be adapted by narrowing the focus to one or two feedback modes (e.g., written or audio), scaffolding the co-creation of feedback criteria with sample prompts or templates, and simplifying the genAI component by focusing on a single tool. Shorter, low-stakes reflections can help build foundational metacognitive awareness, while the rubric can be instructor-curated with student input rather than fully co-designed. These adaptations preserve the collaborative and reflective aims of the assignment while accommodating varying levels of experience with multimodal writing and feedback.
Materials Needed
Time Frame: ~3 weeks in graduate courses; adaptable to 3–4 weeks for undergraduates or condensed into 1–2 weeks for focused modules
Overview: This collaboratively developed assignment engages students in designing and practicing ethical, multimodal feedback in the context of digital and AI-assisted writing. The assignment emerged from a graduate seminar grounded in digital rhetoric, AI literacy, and inclusive pedagogy, and was most recently taught in Spring 2025 ENG 422 (Writing Processes) at North Carolina State University. Titled the Collaborative Feedback Toolkit, the unit guides students through a three-week process, beginning with the co-creation of feedback criteria and ending with reflection on both human- and AI-generated feedback. Students define what meaningful, ethical feedback looks like within their genre and audience, then practice giving and receiving feedback through multiple modes, including written comments, audio, and screencasts. A collaboratively built rubric supports this work, promoting transparency, equity, and contextual specificity. In the final phase, students compare peer and instructor feedback with that generated by tools like ChatGPT, evaluating their relative usefulness and the assumptions underlying them. Reflection prompts explore labor, bias, authorship, and the ethical use of AI in writing instruction. This assignment can be implemented mid-semester or pre-revision in writing-intensive undergraduate or graduate courses, particularly in composition, pedagogy, or digital literacy contexts. It is designed to be flexible and student-driven, with outcomes including improved digital literacy, more thoughtful revision practices, and a stronger understanding of feedback as a rhetorical and ethical act. Ultimately, the Collaborative Feedback Toolkit offers a replicable model for inclusive and critically engaged writing pedagogy in the age of generative AI.
Collaborative Feedback Toolkit
In this assignment, you’ll take part in a collaborative, multimodal, and reflective feedback process designed for tech-saturated writing contexts. Rather than relying on top-down rubrics or single feedback modes, you’ll co-create assessment criteria, experiment with written, audio, and screencast feedback, and analyze how tools like ChatGPT or Claude influence revision. Along the way, you’ll reflect on labor, equity, privacy, and your own goals as a writer and reviewer. Together, we’ll ask: What does effective feedback look like when the writer is human + AI? How do ethics and power shape our responses? Which modes of feedback work best for whom, and why? Feedback is more than correction; it’s how we engage one another’s thinking. This assignment invites you to reimagine feedback as a co-designed, ethical, and adaptable practice.
Step 1: Co-Creating Feedback Criteria
Activity: Our goal is to define what “effective feedback” means for this project, genre, and class.
Deliverables
Step 2: Practicing Multimodal Feedback
Activity: Our goal is to explore various feedback formats and reflect on best practices.
Tools Provided
Deliverables
Step 3: Reflecting on AI Feedback
Activity: Our goal is to analyze and compare feedback from AI and human sources.
Deliverables
Assessment Methods
Reflective Journal
Keep an informal journal in whatever mode you choose to document your experience. Focus on how your feedback practices evolve, what you discover about modality and revision, and how this learning shapes your future writing and teaching.
Final Portfolio Submission
Submit your revised draft with annotations that explain:
Include a 1–2 page (or multimodal equivalent) final reflection on your learning process as a writer, responder, and critical user of feedback technologies.
Suggested Reading List for Contextualizing Discussions
This assignment was collaboratively designed in CRDM 704: Communication, Technologies, and Pedagogy at North Carolina State University, taught by Dr. Kirsti Cole. The design process centers student voice and critical pedagogy, with PhD students actively participating in developing the assignment’s principles, activities, and theoretical foundations. Co-authored by Biven Alexander, Wil Carr, Brody McCurdy, and Bethany Van Scooter, with a special thanks to Tasnim Jannat, Jinzhe Qiao, and Brook Wigginton for their contributions to theory-building, discussion facilitation, and feedback experimentation.
The assignment is grounded in and supported by current scholarship in writing studies, digital rhetoric, and AI literacy. Its emphasis on multimodal feedback draws on research by Mattox and Lopez Guerrero (2022), Sieben (2017), Chen et al. (2018), and Edgerly et al. (2018), which collectively affirm that multimodal, dialogic, and constructive feedback leads to more engaging, personalized, and effective learning. These studies highlight how multimodal strategies (audio, video, screencast, and written comments) foster deeper student revision, support self-correction, and help build inclusive classroom cultures that promote student confidence and writing development. The assignment’s inclusion of co-created rubrics and student-led assessment practices is supported by Joseph et al. (2020), who argue that inviting students into the co-construction of evaluative criteria increases engagement, transparency, and equity. Similarly, Zhang and Chen (2022) emphasize that assessing collaborative writing in digital contexts requires flexible and student-centered approaches, affirming this assignment’s alignment with evolving pedagogical practices in AI-mediated classrooms. The integration of AI-assisted feedback is informed by emerging research showing that tools like ChatGPT can offer functional surface-level commentary but often lack human readers' contextual nuance and rhetorical awareness (FutureEd, 2024; Yomu AI, 2025). This supports the assignment’s emphasis on critically evaluating AI-generated feedback, protecting student agency and authorship, and supplementing—not replacing—human insight. Key influences include Chris Anson’s work on teacher feedback tools (2023), Cheryl Ball’s scholarship on genre-centered assessment (2012), and Mya Poe’s research on equitable feedback and digital writing assessment (2013). The assignment also aligns with best practices outlined by Sedita (2025), who advocates for ethical and reflective AI integration in writing instruction.
The Campus Writing and Speaking Program (CWSP) at NC State provided support for the development and refinement of this assignment. The program's mission to enhance communication pedagogy through multimodal literacy and faculty innovation helped shape both the content and structure of the project. This assignment demonstrates how co-designed, research-informed pedagogy addresses the evolving challenges of digital and AI-assisted writing while fostering inclusive, collaborative, and student-centered learning.