Liping Yang is an incoming assistant professor of English at Kennesaw State University.
Since the moment writing was introduced, it has faced criticism, and the criticism has often sounded like a crisis. Plato, for example, worried that writing, by moving ideas from a speaker’s mind into an external medium, would weaken memory and distance people from real understanding (Jowett, n.d.). More important, he feared that this externalization shift would turn living communication into a static act that cannot answer back, causing alienation from truth-seeking. Therefore, writing was framed as an immediate “literacy crisis.”
That history matters because it reminds us of something writing studies have long understood: Writing is never separate from its tools. The technologies we use to write shape what writing looks like, how it circulates, who can participate, and what counts as (“good”) writing. Across centuries, writing has moved through shifting material forms, such as paper, print, the personal computer, Web 1.0 and 2.0, and now AI. Each shift changes the conditions of writing.
In late 2022, generative AI brought a level of speed, accessibility, and fluency of text generation that left the field unprepared. Almost overnight, the field was flooded with anxious headlines and hot takes lamenting that AI “Destroys College Writing” (Hsu, 2025; Procopio, 2025), and “kills writing careers” (Procopio, 2025). In response, we saw rapid institutional reactions, including “AI ban” policies in K–12 (Carroll, 2025), and early college classroom restrictions shaped by concerns about integrity, learning, and plagiarism policies. We also saw forms of AI refusal (Sano-Franchini et al., 2025) grounded in humanistic values, protecting the importance of human-centered writing and learning experiences. But by 2026, the conversation matured. Gradually, the field began building frameworks rather than only policies—initiatives like critical AI literacy, AI-aware pedagogy, and AI fluency. Even within major journals like Computers and Composition and conferences such as the Conference on College Composition and Communication (CCCC), the momentum shifted toward careful, research-informed engagement. As a result, we see a consistent call for collaboration and intentionality.
The debate hasn’t disappeared, and it shouldn’t. The continuity of the debate reflects a real recognition: AI is already shaping the field’s research and pedagogy in significant ways, including how we position our teaching and practice. But one productive takeaway is this: AI refusal and boundary-setting have value because they clarify what we refuse to lose. At the same time, we can also treat this “technology” as a resource, one that can be leveraged to consolidate humanistic learning and values rather than replace them.
So what can English and writing studies offer in this moment?
As the field navigates this technological turbulence, English and writing studies can respond proactively—not because we have all the answers, but because we have well-developed disciplinary tools for asking better questions. We are uniquely positioned to model responsible, strategic, and ethical AI integration in higher education.
In this blog, I am offering two practical, discipline-grounded scaffolds that can move us from uncertainty to purposeful teaching.
1) Re-center writing as a process (not just a product)
One of the most important things writing studies has taught for decades is that writing is not only a final artifact. Writing is a dynamic, recursive process (Murray, 1972): We engage ideas and resources, take risks, revise, negotiate evidence, explore voices, and navigate our agency as we grasp the “tenor” and “put in oar” (Fister, 2011). We make rhetorical choices. We learn by writing.
When AI is used inappropriately, the process can disappear: Once we get the prompt, we feed it into AI, and boom, there’s a finished piece of writing ready to submit. If the only thing the institution asks for is a final draft, it can feel like the “writing” is over. But just because the submission mechanism collects a final product doesn’t mean teaching should be centered on the final product.
A discipline-strength approach is to double down on writing as process, not as nostalgia, but as pedagogy. If writing is a mode of learning, then we design instruction so the process becomes visible, assessable, and valuable. In an AI context, this means we can integrate AI into the process in ways that support learning rather than shortcut it.
A concrete example: the research paper as a process (with intentional AI “micro-engagements”)
At a WPA workshop I attended, Dr. Jennifer Duncan (Georgia State University, Perimeter College) emphasized how being process-oriented gives us a chance to integrate AI. I want to build on that and extend this lens to further visualize how an AI-supported writing process helps us build strong drafts without erasing the learning. For example, when students write a research paper, they usually follow the process shown below.
In the context of AI, we can treat AI as a tool students can use at each stage of the writing process for specific purposes where students practice different skills, such as information literacy, rhetorical awareness, synthesis, and revision strategies. This way, AI is used in this process as a supplement instead of a shortcut. When students learn how to use AI in contained, purposeful ways, they can further transfer that decision-making across contexts.
In a research paper, AI can be used to weigh the pros and cons of selecting a research method in a specific situation. In a formal proposal, AI can be used to explore external rhetorical factors in the prewriting stage. Those activities keep writing as learning in the center. Students practice using AI as support during thinking and writing processes rather than as a shortcut.
2) Teach rhetorical agency and metacognitive reflection in AI-mediated writing
If the first commitment is “writing is a process,” the second is “writers still steer.”
In the AI-supported curriculum that I lead with my team members, we emphasize students’ “rhetorical intelligence” (McKee and Porter, 2019) when working with “machine intelligence.” The goal is to situate AI use inside disciplinary strengths by foregrounding agency.
During AI engagement, students negotiate meaning with the model, interpret outputs, and make decisions about what to accept, reject, revise, or discard. That negotiation is rhetorical work. And it is also where risks emerge, because AI outputs can carry “ideologies,” assumptions, biases, and misinformation. If students outsource judgment, they can be harmed by those embedded frames. If students remain the “captain” of this AI engagement, they can learn to steer. That is why intentional engagement matters. And one of the most effective tools we have for teaching intentional engagement is metacognitive reflection.
Reflection as a framework: before, during, and after AI use
Previously, I collaborated with Dr. Michael Harker (professor of English at Georgia State University) on an assignment and publication that supports students’ AI engagement through a scaffolded reflection guide. The structure is shown below.
- Planning. Before AI engagement, students make a plan for engagement, think about what they aim to accomplish through the engagement, and consider what this reveals about their writing challenges.
- Critical evaluation. During AI engagement, students don’t take the output for granted but instead examine underlying bias, misinformation, or assumptions. This way, they keep a critical eye on AI. This is also where students practice information literacy in a new context. They learn not to treat output as “truth,” but as text that demands rhetorical reading.
- Transfer and learning. After AI engagement, students reflect on how this changes their writing process, knowledge, and feelings, and how they can transfer and build on this experience in a different context. This framework helps students appreciate AI’s affordances and limitations while engaging in a reflective practice that holds productive tension; they maintain boundaries, but still learn to use AI critically as a resource in the ongoing evolution of writing technology.
For a more detailed look at how this works in practice, I’ve included the full activity sheet here (link) from TextGenEd (Schnitzler et al., 2025). The guide walks students through structured planning, critical evaluation, and post-engagement reflection to help them maintain agency in AI-mediated writing.
Conclusion
If we zoom out, the “AI crisis” in writing studies begins to look familiar. Like earlier moments of technological disruption, it forces us to clarify: What is writing for? What do we mean by learning through writing? How do we teach students to write and think, not just produce text? And that is exactly what this AI moment demands.
Generative AI didn’t end writing. It exposed what writing teachers and scholars have been arguing all along: Writing is a process, a practice, and a set of choices that can’t be reduced to a polished paragraph on a page. If we respond by strengthening process-based pedagogy and teaching rhetorical agency through metacognitive reflection, we don’t just “adapt” to AI, we shape what responsible AI-supported writing can become. Because at the end of the day, writing has never been only about producing words. It is about making meaning, building knowledge, and entering conversations with purpose.
References
Association for Writing Across the Curriculum. 2025. “Statement on AI and Writing Across the Curriculum – Association for Writing Across the Curriculum.” Association for Writing Across the Curriculum. https://wacassociation.org/ai-statement/.
Carroll, Robert. 2025. “New Legislation Would Ban AI from New York’s Schools.” The 74. https://www.the74million.org/article/why-ai-doesnt-belong-in-schools/.
Delgado, Paulo. 2026. “AI Killed My Writing Career. Long Live AI. | by R. Paulo Delgado.” The Writing Cooperative. https://writingcooperative.com/ai-killed-my-writing-career-long-live-ai-ecd264cd4d9f.
Fister, Barbara. 2011. “Burke’s Parlor Tricks: Introducing Research as Conversation.” Inside Higher Ed. https://www.insidehighered.com/blogs/library-babel-fish/burkes-parlor-tricks-introducing-research-c…
Hsu, Hua. 2025. “What Happens After A.I. Destroys College Writing?” The New Yorker. https://www.newyorker.com/magazine/2025/07/07/the-end-of-the-english-paper.
Jowett, Benjamin. n.d. “Phaedrus by Plato.” The Internet Classics Archive. Accessed March 27, 2026. https://classics.mit.edu/Plato/phaedrus.html.
McKee, Heidi A., and James E. Porter. 2019. Professional Communication and Network Interaction: A Rhetorical and Ethical Approach. N.p.: Taylor & Francis Group.
MLA Style Center. 2024. “Student Guide to AI Literacy.” MLA Style Center. https://style.mla.org/student-guide-to-ai-literacy/.
Murray, Donald M. 1972. Teach Writing as a Process Not Product. N.p.: New England Association of Teachers of English. https://mwover.com/wp-content/uploads/2018/05/murray-teach-writing-as-a-process-not-product.pdf.
The Ohio State University Office of Academic Affairs. 2025. “AI Fluency | Office of Academic Affairs.” Office of Academic Affairs. https://oaa.osu.edu/ai-fluency.
Procopio, Joe. 2025. “Has AI Destroyed Writing?. YES | by Viam | Behind The Words.” Medium. https://medium.com/behind-the-words/has-ai-destroyed-writing-7c53fcc5f258.
Sano-Franchini, Jennifer, Megan McIntyre, and Maggie Fernandes. 2025. “Refusing GenAI in Writing Studies: A Quickstart Guide.” Refusal. https://refusal.blog/.
Schnitzler, Carly, Annette Vee, and Tim Laquintano. 2025. “An Introduction to the August 2025 Collection.” The WAC Clearinghouse. https://wacclearinghouse.org/repository/collections/continuing-experiments/august-2025/.
Tham, Jason. 2025. Computers and Composition. https://www.sciencedirect.com/journal/computers-and-composition.
MEET THE AUTHOR
Liping Yang is an incoming assistant professor of English at Kennesaw State University. Her research focuses on composition theory and pedagogy, computational rhetorics, and technical and professional communication. Besides teaching a range of writing courses, she has published widely in disciplinary conferences and journals, with her work appearing in publications by Peter Lang and Taylor & Francis.