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The piece explains how large language models generate text from probabilistic patterns in training data, illustrating prompt-based continuation, and situates current debates within a long history of technology-related moral panic in writing studies.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/post-process-but-not-post-writing-large-language-models-and-a-future-for-composition-pedagogy/599416/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/post-process-but-not-post-writing-large-language-models-and-a-future-for-composition-pedagogy/599416.png","ImageObject",300,407,{"name":42,"@type":43},"WPS_1790064749","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-10-15",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",5,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why does the essay reject concerns about AI harming student writing?","Question",{"text":63,"@type":64},"It argues the worries assume an overly limited, linear view of writing. 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Scott Graham  \n[S]tarting with a blurry copy of unoriginal work isn’t a good way to create original work. If you’re a writer, you will write a lot ofunoriginal work before you write something original. And the time and effort expended on that unoriginal work isn’t wasted; on the contrary, I would suggest that it is precisely what enables you to eventually create something original. The hours spent choosing the right word andrearranging sentences to better follow one another are what teach you how meaning is conveyed by prose… If students never have to write essays that we have all read before, they will never gain the skills needed to write something that we have never read.  \n—Ted Chiang  \nThe above passage from  \nbecome stock-in-trade  \nTed Chiang’s New Yorker essay on ChatGPT has for critiques of using AI in student writing and  \nwriting pedagogy. Indeed, the internet now abounds with confident complaints about the damage ChatGPT and other AI systems will invariably do to student writing. These new technologies have subsequently been heralded as the end of the essay and a substantial threat to writing education (Marche) . The common arguments about potential student use of these technologies suggest AI-assisted writing will damage student learning by shortcutting the writing process. They further worry that AI-based pedagogy will de-skill students by reducing writing to a mere editing practice. Additionally, some suggest that students will fail to do adequate background research, relying on dubious or non-existent AI-generated sources. Ultimately, these concerns indicate a view of AI-assisted writing where students can essentially parachute their way into the last stages of the writing process, skipping past considerable effort and learning. I largely reject these worries because they simply don’t fit with my understanding of how writing works. At their core, each of these concerns is marked by a problematic commitment to a limited and linear process-based model of writing. Instead, I think writing instructors and scholars of writing studies can consider how our hard-won insights about diverse and variable writing practices might allow for robust writing pedagogies that engage, rather than prohibit, use of AI.  \nWhile engaging with AI maybe uncomfortable for many, I think it should be considered a requirement for two reasons. First, the history of writing stud  \nComposition Studies 51.1 (2023): 162–168  \nies is a history of moral panic about new technologies. From Plato’s laments about inscription itself to later rejections of erasers, typewriters, word processors, SMS, and spell-check as inimical to writing, writing studies was always already worried about the effects of new technologies. However, in each of these cases, we learned how to adapt our pedagogy to new realities and the end results have been that students now write more than ever before without any measurable decline in quality (Lunsford and Lunsford). Secondly, prohibition has always been doomed to failure. While I genuinely wish we lived in a society that thought carefully and strategically about adopting new technologies, we do not. To teach students about writing without engaging the writing technologies they have access to and will invariably use does them a substantial disservice. It reminds me of the early days of the word processor when professors who had learned to write in an earlier era would compel submission of hand-written drafts because they knew (they just knew) that pencils produce better writing and thinking. If you agree with me that we don’t want to be the AI-equivalent of the pencil mandator, then we need a robust foundation for our AI writing efforts. The good news is that we already have one in post-process pedagogy; that’s the main argument I will be making in this short essay. However, before I fully elucidate this argument, I’d like to s","cbCaidQSomksWiY6","https://ap.wps.com/l/cbCaidQSomksWiY6","pdf",117910,"English","# Post-Process but Not Post-Writing\n## AI concerns about student writing\n## Writing studies and moral panic\n## What even are these things?","[{\"question\":\"Why does the essay reject concerns about AI harming student writing?\",\"answer\":\"It argues the worries assume an overly limited, linear view of writing. Instead, writing instruction can adapt to AI use through robust, engaging pedagogies grounded in diverse writing practices.\"},{\"question\":\"What historical context does the essay use to address AI in writing education?\",\"answer\":\"It describes writing studies as repeatedly responding to moral panics about new technologies, from early anxieties about inscription to later rejections of erasers, typewriters, word processors, SMS, and spell-check.\"},{\"question\":\"How do large language models generate text according to the essay?\",\"answer\":\"LLMs learn from large datasets and model common linguistic probability distributions. Given a prompt, they use those learned patterns and probabilistic continuation to produce statistically likely next words or longer responses.\"}]","Post-Process but Not Post-Writing - Large Language Models and a Future for Composition Pedagogy | PDF",1792088727,18]