AI is Manna for Writing Studies or,
How to stay calm in troubled times
Jennifer Trainor—San Francisco State University
In 2024, Nupoor Ranade and Douglas Eyman summed up a now familiar feeling of overwhelm regarding automated writing technologies:
Following the release of ChatGPT a surge of interest across the media and in communities of teachers and educational technologists lead to a flurry of blog, medium, and substack posts, as well as opinions in editorials in trade journals. . . . In the year that followed, “AI” has become a top-level category at IHE, in recognition of the continuous high volume of news reports and opinion pieces on the topic. Posts and commentaries presented arguments and alarms, ranging from hailing generative AI as finally allowing universities to ditch First Year Writing courses (Nicolas, 2023), to a utopian vision of AI teaching assistants and graders. . . . Those of us trying to keep abreast of AI news were overwhelmed with the daily output just from writing teachers alone, not to mention AI researchers, scientists . . . policy makers, and the AI companies themselves. (1)
The overwhelm Ranade and Eyman describe is layered, perhaps especially for writing teachers, with a sense of crisis. Overblown media panics about cheating seem to collide daily with realities on the ground, as teachers grapple with assignments littered with synthetic text. At campus-wide committee meetings, administrators and non-writing faculty are newly armed with arguments about efficiency (do you really need small classes, now that AI can give feedback?—a question unfortunately circulating on my campus right now). In text exchanges with colleagues at other institutions, worries proliferate: will our required courses survive? What about linguistic justice? How do we safeguard academic integrity? Teach information literacy? Should we change how we teach the writing process? And in hallway conversations, I routinely hear comments like these: I can’t keep up with another AI resource list; I’m tuning out re AI; waiting for retirement; I don’t have time to revamp my entire curriculum; I need a break from AI doom scrolling. I’ve heard versions of these comments so many times—colleagues ignoring AI out of overwhelm and anxiety—that I started using a picture of an ostrich with its head in the sand on my opening slides for faculty development meetings.
The word I keep landing on to describe this intermingling sense of overwhelm and doom is textapocalypse. Originally coined by Matthew Kirschenbaum in a prescient Atlantic essay about AI slop, the word, for me, captures many teachers’ sense that there is too much to read, that our journals and meetings are all AI all the time, combined with a related sense that our work as writing instructors is threatened, and that students’ literacy education is being co-opted for profit, irrevocably harmed.
Literacy crises are not new, of course. But this moment does feel perilous: What’s the future of our work? How do we move forward in our classrooms now? How do we guide new and veteran writing teachers, who often have wildly different responses to this technology? How do we revamp—or find the time to revamp—our curricular approaches to ensure students get the literacy education they need and deserve? As Annette Vee, Tim Laquintano, and Carly Schnitzler write, “It will take all of us to respond to this moment . . .[G]enerative AI is the most influential technology in writing in decades—nothing since the word processor has promised as much impact. And generative AI is moving much faster” (TextGenEd).
This review—of public-facing scholars in our field who are addressing AI in substacks, social media, and online collections of teaching strategies—attempts a partial answer to our sense of overwhelm and crisis. It does so by connecting new to old in our field, and reframing textapocalypse as manna. Manna literally means “what is it?”—as good a first-principles question as we could ask for right now. But it also means “abundance,” and “gift.” AI is a gift in that it has shone a bright light on questions and issues with which our field has long wrestled, imbuing these questions with new relevance and urgency.
Accordingly, this review is not organized in the familiar “resist” vs. “lean in” camps that often structure campus discussions about AI technology and that, I believe, worsen our collective sense of foreboding. I’m suspicious of the “camps” discourse as a false set of choices that limits our thinking, causing us to miss the echoes and amplifications between the AI conversations we’re having now and older lines of inquiry in our field. Camps discourse obscures how much work we’ve already done—decades of scholarship on the social, material, mechanical, embodied, and political dimensions of literacy—that prepare us to meet this moment.
In tracing a few of those conversations here, I pay homage to those who public-intellectualed early guidance and understanding of the implications of AI, and who, in some cases, have been focused, in their scholarship, on writing technologies since well before the release of Chat GPT. Their perspectives suggest we can address the textapocalypse by viewing AI as “normal technology”—not that we normalize AI, but rather that we connect the questions it raises to questions we regularly wrestle with in our field and our classrooms. As Antonio Byrd writes, “AI and writing requires more thoughtful, careful rhetorical and ethical frameworks” (“Truth-Telling” 140). Note that he doesn’t say “new,” that instead he resists crisis discourse, insisting only that we continue the work we’ve always done. The scholars reviewed here were chosen to represent these normalizing connections, to help teachers and scholars who feel adrift find some grounding, and to highlight some starting points of entry for teaching faculty who have not been given time to read and educate themselves, and hence who may be feeling this moment of overwhelm and crisis in acute ways.
REFUSE AI: WHAT WOULD PAULO FREIRE SAY?
The first time I felt firm ground under my feet post-ChatGPT was last year, when a colleague sent me Jennifer Sano-Franchini’s 2025 CCCC keynote address. The keynote was my introduction to the Refuse AI movement in Writing Studies. I think of the scholars behind this movement as the critical pedagogues of the GenAI era, Luddite Freireans who situate their critiques of automated writing technologies in bracingly familiar terrain: literacy and language justice; students’ rights to their own languages; and the relationships between literacy, culture, power, and social justice.
At the heart of AI refusal is a principled stance against the nihilism of automated writing: AI’s tendency toward linguistic and cultural erasure, its corporate homogenizing of language, and its alignment with authoritarian and colonial logics. These issues are baked into the technology, scholars like Sano-Franchini, Megan McIntyre and Maggie Fernandes argue, and hence principled refusal is warranted.
Publicly, Sano-Franchini, McIntyre, and Fernandes’ work circulates through the Refuse AI blog and website, which offer theory and guidance for resisters. Their quick start guide for resistance names the harms AI brings and frames them in commitments our field has long cherished. In a recent post on their blog, Fernandes reflects on being an AI killjoy, using Sara Ahmed’s theoretical framing: “killjoys kill the fun by naming systemic sexism, queerphobia, transphobia, racism, and ableism, and very often, they are held responsible for the unhappinesses they name—the unhappiness caused by systemic oppression” (“On Being A GenAI Killjoy”). The Refuse AI website spotlights writers like Alfred Owusu-Ansah. In “Defining moments, definitive programs, and the continued erasure of missing people,” Owusu-Ansah shows how AI positions linguistically minoritized people as outside public understandings of what it means to write: in his experiments with ChatGPT, he finds the tool “echoing decades of imperialist framing that positioned English varieties of the Global South as being diametrically opposed to the English varieties of the North” (144).
Automated writing, for AI Refusers, is thus an arm of authoritarianism and colonialism, giving rise to epistemicide, or what Freire once called the “necrophilia” of banking approaches to education. In this view, the horror of AI is not the sci-fi scenario of machine sentience but rather the deadness that accrues to algorithmic word prediction in place of human meaning making. Several non-Writing Studies folks have made this case—Emily Bender, Ruha Benjamin, Audrey Watters, Brian Merchant—but Sano-Franchini’s keynote and the Refusing AI blog connect these issues to core values in our field.
The Refusing AI website is by definition light on teaching ideas and classroom strategies, but the authors’ approach dovetails with emergent online resources for curriculum building, such as the MLA Task Force teaching archive: Christopher Jimenez’s Teaching Social Identity and Cultural Bias Using AI Text Generation; Anna Mill’s Testing Bias in Google Search; and Cindy Tekobbe’s Critical AI and Indigenous Story-Telling. And Fernandes, McIntyre, Kat Gray, and Cara Marta Messina have curated an extensive student-facing “Critical AI” reading list for those who want to cultivate critical AI literacy in their classrooms.
Interestingly, Refusing AI’ers also reject some of the solutions to student AI use that routinely pop up on my campus, including process tracking (note that they are not opposed to teaching students to engage in the writing process but rather question the wisdom of surveilling students’ process as an antidote to AI). In “Drafting Defensively, Documenting Authorship: An Analysis of Draftback and Grammarly Authorship,” McIntyre and Fernandes argue that process tracking technologies like those being marketed by Grammarly are essentially “process surveillance interfaces” that “reinscribe normative values for writing as process and facilitate feelings of suspicion, anxiety, and defensiveness for users” (76). Given alarm about edtech companies’ pivot toward AI (see Marc Watkins’ recent post, An Open Letter to Perplexity AI), and their turn toward problematic marketing pitches that undermine academic integrity and weaken public understanding of the non-transactional dimensions of writing, this essay is worth a read. It’s a reminder that technology companies are selling us not just problems, but also, conveniently, the solutions to them. Indeed, my colleagues, Martha Kenney and Martha Lincoln, at SF State make the case, in a blistering article about Cal State’s “partnership” with Open AI:
these AI “partnerships” play into dynamics that concentrate wealth and power into the hands of the few, while potentially eroding the quality of higher education—a process the journalist and science fiction author Cory Doctorow calls “enshittification” . . . . This is a particularly ironic outcome given the tendency for universities to describe AI as a panacea for higher education’s woes and a bold step into the future of research, teaching, and learning. Against these optimistic assumptions, we contend that universities may be unwittingly preyed on by an industry they tout as a partner. (7)
Like the Luddites and killjoys before them, the Refuse AI’ers may not win the day. As many relentlessly point out, this technology is here to stay, and students are using it. Under such conditions, resistance is not easy. But the Refuse AI website—and adjacent resources like Fernandes’ and McIntyre’s podcast, “Everyone’s Using AI Except” which contains interviews with a variety of AI skeptics—offers strategies for resistance and community to ease the way.
Technological Sponsors of Literacy
Where the Refuse AI movement resists, other public voices in our field have been pushing us to think more about how technology sponsors (not just disrupts) literacy. Antonio Byrd is a leading example. His scholarship examines coding and computing as literacy practices within Black communities. Like Deborah Brandt, whose materialist sponsors of literacy framework showed the surprising non-school twists and turns learners take on their route to literacy, Byrd focuses on coding camps as sponsors of literacy and empowerment in African American communities. His recent work extends this inquiry into the AI era, arguing that large language models are not just tools but infrastructures that shape—and can be reshaped by—our values.
In the post-ChatGPT era, Byrd has become a strong voice for a deep understanding of the materiality of LLMs. In a 2023 Composition Studies article, he agrees with the Refuse AI critiques that LLMs homogenize language and erase linguistic diversity; they have been trained on racist language and ideologies. But from there, rather than arguing that we need to avoid LLMs because of these problems, he argues that as language scholars and educators, we have a responsibility to ensure that the training data of the future reflects our values and our students’ voices, to protect against continued, future erasure. Put another way: we have an even greater responsibility, now, to eschew linguistic violence in the work we ask students to do, as their writing will become part of the training data for LLMs of the future:
[W]riting classrooms can be a counterweight to the historical moments that turn into corpus texts, with marginalized social identities now more prominent on the mainstream stage than ever before, and our pedagogies shifting (although slowly) toward linguistic justice and care. Writing instructors across disciplines have an opportunity to partner with their students on creating digital content that the next iteration of LLMs will one day scrape. To this end, we are positioned to launch critical inquiries on corpus texts: how they are made, what they contain, how they shape our own literacy practices when they filter through the literacy practices of LLMs, and how we participate and write in the histories that LLMs consume. (Byrd 138)
Here Byrd argues for engaging students in corpus analysis and ethical data creation and sees these practices as acts of resistance to oppressive literacy systems. Technical as his writing and ideas may sound, this perspective is rooted in familiar materialist, non-school literacy research like Brandt’s, and the work of Katherine Kelleher Sohn. Kelleher Sohn, in her 2006 book, Whistlin’ and Crowin’ Women of Appalachia: Literacy Practices Since College, examined the non-school literacy practices of women in Appalachia, for whom writing was embedded in economic struggle as well as a vehicle for personal empowerment. Like Brandt, Sohn focuses on literacy’s accumulations, its “layers of earlier forms of literacy [that] exist simultaneously within the society and within the experiences of individuals” (Brandt, qtd in Kelleher Sohn 55). Kelleher Sohn notes as well “vertical layers of reading and writing”—intricate layerings of incentives, sources and barriers that learners must negotiate. Byrd asks us to see these layers as they exist in current and future LLMs, and to consider our role in preparing students to navigate them.
Like Sofia Noble, Joy Buolamwini and Ruha Benjamin, Byrd sharply critiques the whiteness and racism underpinning technology. But also, like Brandt, Ellen Cushman, and more recently Kate Vieira, Byrd is ultimately interested in a broader understanding of literacy and technology. Vieira, for example, examines literacy as “communication hardware, software, writing practices, and literacy knowledge that migrant families often circulate” as they cross borders (Vieira 4). Like these writers, Byrd is less focused on systemic critique than on systemic navigation—how individuals, including teachers, can hack their way into inhospitable territory, acquiring new literacy practices along the way. He encourages teachers to see technology in similarly expansive ways.
As a public scholar of the AI era, Byrd has helped create many emergent online resources, including the MLA task force website, which is a treasure trove of resources and guidance. He has a talk on YouTube on academic integrity and one on equitable teaching in the age of AI. In both of these videos, Byrd frames teaching tips with more theoretical and technical understandings; viewers will find teaching strategies alongside theory. In his video on AI and academic integrity, he quotes Brian Street and Kate Crawford: AI is a “registry of power,” serving “dominant interests”; literacy is a social practice, embedded in social systems, rather than a neutral set of skills. Here Byrd presents academic integrity as something we use or do, rather than a static quality to be protected or policed. This aligns with his interest in agency and navigation: how do students enact academic integrity? How do the systems and culture in place in our institutions support or undermine students’ navigation of academic integrity practices?
On Byrd’s LinkedIn page, he regularly reposts a variety of AI and writing-related articles and information. I learned about MIT’s AI Incidence Tracker from his LinkedIn; I found Elizabeth Wardle’s recent article in entrepreneur magazine, cogently explaining the importance of writing for thinking and human connection; I discovered this report from Anthropic on how students are using AI in education. Byrd also often reposts Anna Mills’ invaluable contributions to our AI conversations. I wasn’t able to fold Mills’ work into this review, but she is also an important voice who has helped shape our field’s response to this moment.
Byrd’s work may be technical, but he brings the technical and the social-political dimensions of literacy into provocative view, extending Brandt’s framework of literacy sponsorship to the age of AI.
Machine-in-the-Loop Composing Processes
Another first responder who has made a sizable contribution to our AI conversations is Jane Rosenzweig, who started “The Important Work” substack in 2023. Aimed at broadening the conversation about AI and writing, “The Important Work” features personal reflections from teachers across ranks and contexts, including high school, about their classroom experiments with AI. Since its launch it has evolved into a teaching commons where instructors reflect on what we’re gaining and what we’re losing when we bring AI into the writing classroom.
At “The Important Work,” you’ll find explorations of how students use AI in their writing process as well as how teachers are reinvigorating our old adage—process over product—as a way to recenter and protect students’ opportunities to engage in writing as inquiry. In an essay I particularly love, Danielle Kane and Claire Masson redesign in-class writing away from the punitive timed blue book tests of old and toward community writing to support student voice and agency. In another post, “What Are Students Using AI For,” we gain an up-close glimpse into students’ machine-augmented writing processes.
Rosenzweig is a creative writer and the director of the Writing Center at Harvard, where Nancy Sommers published her seminal essay on the composing processes of novice and experienced writers, an essay that has always been an anchor point in my first-year writing classes. Rosenzweig’s motive in starting “The Important Work” was her sense that we’ve “entered a new era in which the lines between process and product [are] no longer clear.” Here she echoes Jason Guyla, who has recently argued that an uncritical embrace of process as a way to address AI risks flattening out and standardizing writing processes, which are messy, idiosyncratic, context-specific, and not well-understood. As Laquintano, Schnitzler, and Vee write “[o]ur writing environments will inevitably be shaped by these AI integrations, but it’s unclear what effects this integration will have on our writing or writing processes” (TextGenEd).
This risk and the critique of process that emerges from it has existed since long before AI (more on this below). It may be more difficult than ever to understand—and design curriculum that adequately addresses—students’ changing writing processes in the era of AI. But understanding student writing processes has always been a difficult, and central, core of scholarship in our field. Consider this, from Linda Flower and John Richard Hayes in 1981: “[S]tage models [that distinguish between] the operations of planning, writing, and revising may seriously distort how these activities work. For example, Nancy Sommers has shown that revision, as it is carried out by skilled writers, is not an end-of-the-line repair process, but is a constant process of “re-vision” or re-seeing that goes on while they are composing” (367).
So it is perhaps not surprising that writing process scholarship is undergoing something of a renaissance now, when technology seems to be obscuring the mysteries of process more than ever. In “Machine-in-the-Loop Writing: Optimizing the Rhetorical Load,” Alan Knowles, for example, introduces “rhetorical load sharing” as a way to conceptualize writing processes undertaken in collaboration with technologies. His article is a deep dive into the classical stages of process (from inventio to arrangement to delivery), exploring whether and where AI can share the load with human writers. He distinguishes between cognitive off-loading and rhetorical load-sharing, and makes some provocative points about the nature of collaboration in general, drawing from John Gallagher, Kyle Wagner and Jordan Canzonetta’s essay, “When Collaborating Turns into Dishonesty: A Data-driven Heuristic Comparing Human and AI Collaborators.”
So can machines lighten the rhetorical load? Is there a place for machines in the non-linear process of re-vision that is so central to how we teach writing? Knowles, like the writers at “The Important Work,” is cautious. Citing safety and accuracy concerns with LLMs, Knowles recommends that we relegate machines to a largely assistive role, with humans maintaining the lion’s share of control over each aspect of the rhetorical load. This accords with Adrian Rowland, who teaches scientific writing. In a post at “The Important Work,” he writes: “In my experience of experimenting with their application to scientific text, text written by my students and by members of the chemistry faculty for whom I have provided editing assistance, LLMs are highly effective at improving cosmetic aspects of prose, but even with specific prompting cannot be relied upon to fix problems of meaning: to identify that a paragraph has failed to state its real point or that an explanation has made an excessively large leap in the middle, to correct sentences that are misemphasized or ambiguous or outright wrong” (“The Important Work”).
Knowles argues for human-in-charge drafting all the way up to the final stages of the writing process. At The Important Work, most writers agree, and are tinkering their way toward curriculum that relegates AI to just another tool in a writer’s toolkit.
One of the strongest voices among first responders in our field is Annette Vee. Perhaps as much as anyone, she has done the heavy lifting of contextualizing an alien technology in familiar pedagogical terms. Vee has co-edited the excellent TextGenEd collection and is a co-editor of the forthcoming AI-Aware Teaching. She has two, equally excellent, substacks: her own, “Computation and Writing,” and a Norton newsletter, “AI and How We Teach.” In a recent post from this past September, “AI and Student Agency,” Vee argues for recommitting to student agency and collaboration as a way to address AI use in the writing classroom. Other posts include quantitative and qualitative data on how students use AI, as well as detailed teaching ideas and assignments, complete, in some cases, with her classroom slide decks. Like Mills and Marc Watkins (another strong voice who has carried so much of the work of guiding teachers over the past few years), Vee synthesizes her deep understanding of the technology with empathy for overwhelmed teachers, and an abiding curiosity about students’ perceptions and practices.
With Laquintano and Schnitzler, she has written a history of automation and writing (in their introduction to TextGenEd) that should be required reading for writing teachers. Throughout, Vee and co-authors show us the imbrications of writing with technology, and AI research, extending well before the release of Chat GPT: in the 1950’s,
Turing speculated on a prompt from his teacher, philosopher Ludwig Wittgenstein: Can machines think? Both men thought it was a ridiculous question—Wittgenstein because he thought machines were nothing like humans and Turing because he wasn’t even sure we knew what humans thought. But, Turing argued that if a machine could fool a human into thinking it was a human, then it could be said to think. The machine—a computer—would naturally use writing for this deception. Writing, in other words, is thinking—and the automation of writing is machine thinking. (3)
As I’ll discuss in my conclusion, historical perspectives—knowing that many in our field have been exploring and conducting research about automation and writing for decades—can be an important grounding that protects against AI overload and anxiety.
A colleague of the late David Bartholomae, Vee extends his vision of writing as identity-building, inquiry, and participation in disciplinary discourse. She often reframes AI not as an existential threat but as an invitation to collaborate with students, to put them in charge of meaning-making. Bartholomae’s work helped a generation of composition instructors understand the importance of this kind of student agency, of treating students like novice members of our community. He was famously against a basic skills approach to basic writing, even as he argued for students’ inculcation into academic ways of reading and writing. I see this commitment to what Adam Banks called “transformational access” in Vee’s work as well. Her substacks channel this perspective in a variety of practical, accessible posts about how students are using AI, about how to think about the risks of AI, and how to adapt assignments to make them “AI-aware.” Throughout these posts, she centers student agency and recommends partnering with students in the classroom.
It is hard to imagine that Bartholomae would have had any love for automated writing, and yet Vee manages to invoke Bartholomae’s legacy, not to critique AI but to face, and in some ways embrace, it. In a beautiful essay in Composition Studies, she pays tribute to Bartholomae, who passed away in 2023, as she attempts to think through what AI might mean for the kind of writing classroom Bartholomae pioneered:
Making productive use of uncertainty runs counter to what large language models (LLMs) such as ChatGPT represent, and even what ChatGPT outputs when I ask it about the value of writing: to explain, to argue, to persuade. ChatGPT is—infamously—never uncertain. It responds with confidence if it’s right and even if it’s obviously, tragically wrong. More importantly, it has no relationship to what it means to be uncertain, to inquire, to examine its own experiences. It has no stakes in what it writes.
The writing for critical inquiry that our first year students do isn’t about projecting confidence. It’s not about getting points in a row with a thesis statement that lays them out and a conclusion that restates them. Writing for productive uncertainty isn’t that neat or simple. It’s based on the idea that students can thrive wrestling with difficult texts, that they can come up with new and important questions about their worlds and their own words. The point isn’t what kind of writing students produce, exactly, although writing is the medium students use to pursue their inquiry. The point is the process they went through to get there. The point is the challenge: the pleasurable difficulty of writing and reading. (177)
I find Vee interesting because of her effort to bridge that perspective with her interest in technology, and her non-panicked approach to students’ use of AI in their writing. Vee makes me realize that as much as Bartholomae would have detested a ChatGPT-ed essay during one of his infamous basic writing seminars, he also would have been deeply curious about new affordances, about what such a moment might represent, about the many possible questions it opens up for classroom inquiry and meaning making.
Vee, like Bartholomae, sees pedagogical value in students’ negotiations with systems of authority—whether textual, institutional, or technological. Bartholomae’s insight in “Inventing the University” was that students learn to write not by escaping authority, but by inhabiting their own version of it. His curiosity about student error and imitation was ultimately about how students construct knowledge by testing themselves against, and by working within, the language of power. Vee’s curiosity toward students’ use of AI echoes this stance. She’s not lamenting AI as the death of voice but asking what new forms of collaborative authorship and agency might emerge as students learn to write with, or against, these systems. Like Bartholomae, she’s interested in how students appropriate a dominant discourse—in this case, machinic or algorithmic language—and make it mean something personal or situated, purposeful or learningful. Many have decried the robovoice that AI encourages in students, and some, like John Warner, have connected that voice to the inauthentic voices that our standardized approach to writing in school has encouraged in students. Warner, like so many of us, wants to rescue student “voice” from AI. But Vee encourages us, in her Substack and in the curricular work she’s been part of—to treat AI as a generative, collaborative problem-space: as she writes, “We have never written alone, and students are now writing in the company of AI. We can acknowledge that fact and support them as they learn to write effectively in collaboration—with humans or AI.”
In this, Vee echoes Bartholomae, who imagined voice not as liberation from authority but as transformation through it; as he wrote, the student who uncomfortably takes on an authoritative voice they don’t yet (may not ever) fully own is engaged in a “necessary and enabling fiction.” Of course, there is a world of difference between momentarily inhabiting the voice of a scholar, or trying on Richard Rodriguez’s sentence rhythms, and cut and copying synthetic prose that sounds official but says nothing. And yet, here are Vee and Tim Laquintano on “voice” as a valuable framework used by everyday writers in their negotiations with machines: “For many of the writers in our study, voice exists as a metaphor framing a bundle of concerns related to AI text generation and machine-assisted composition. It helps writers generate heuristics of value as they decide what practices can be—or should be—off-loaded to a machine” (“Everyday Writer”).
In these ways, Vee is pushing us to reinvent the university, to make clear Bartholomae-esque distinctions between authority (whether synthetic or teacherly) and authorial for students, to see the affordances, for learning and meaning-making, in borrowing or trying on others’ words, whether those words are derived from generative textual commonplaces of the They/Say I/Say variety, or other authors or machines.
It’s common these days to point to tech panics of the past to calm fears of AI (Watkins has recently reminded us of the fallacy of the new, in this post). In the connections we see between old and new in the writers I’ve reviewed here, I have tried to show where we’ve been in order to clarify and ground where we are. I’ve been less interested in how we may have worried about past challenges and changes, and more interested in how we have built—and are building— from them. The tech panics of old are amusing (if one more person brings up Plato…) but it’s bracing to hear Gail Hawisher and Cynthia Selfe in 1991: “Computer technology offers us the chance to transform our writing classes into different kinds of centers of learning if we take a critical perspective and remain sensitive to the social and political dangers that the use of computers may pose” (56).
And in 1998: “students must develop technical literacy practices that go well beyond the conventional conception of literacy education” (Selfe and Selfe 358).
What dangers were Hawisher and Selfe predicting, all the way back then? What “technical literacy practices” did they have in mind? The answers, in some ways, are so obvious to us now as to be inaccessible, invisible, perhaps much like our experience of textapocalypse will someday be.
Same as it ever was. Here is Elizabeth Sommers, a professor of English at SF State, my own institution, writing in the 1980s:
Microcomputers do have exciting possibilities as writing tools if they are used well. The problem is separating the many ineffective uses from the good ones . . . Most of what we learn is learned in cooperation with others, and writing is no exception. The most useful thing we can do for student writers . . . is to provide audiences for their writing, audiences who will read and respond in supportive and helpful ways. . . . Computer-assisted instruction can help, but cannot take over the central roles played by writers and respondents.” (4, 7, 8)
Fear not, all ye who are worried and overwhelmed. We’ve been here before.
Works Cited
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Buolamwini, Joy. Unmasking AI: My Mission to Protect What is Human in a World of Machines. Random House, 2024.
Byrd, Antonio. “Black Tech Ecosystems.” U of Michigan P, forthcoming.
—. “Truth-Telling: Critical Inquiries on LLMs and the Corpus Texts That Train Them.” Composition Studies, vol. 51, no. 1, 2023, pp. 135–42, https://compstudiesjournal.com/wp-content/uploads/2023/06/byrd.pdf.
Cushman, Ellen. The Struggle and the Tools: Oral and Literate Strategies in an Inner City Community. SUNY P, 1998.
Fernandes, Maggie, and Megan McIntyre. “Drafting Defensively, Documenting Authorship: An Analysis of Draftback and Grammarly Authorship.” Computers and Composition, vol. 76, 2025, 102926. ScienceDirect, https://doi.org/10.1016/j.compcom.2025.102926.
Fernandes, Maggie, and Megan McIntyre, hosts. “Everyone’s Writing With AI (Except Me!).” Spotify, 2025, https://open.spotify.com/show/61rtA0ObNmHEIQlLk4a4eg?si=07802c982db94628.
Fernandes, Maggie, Megan McIntyre, and Jennifer Sano-Franchini. “Refusing GenAI in Writing Studies: A Quickstart Guide.” Refusing Generative AI in Writing Studies, 2025, https://refusinggenai.wordpress.com/.
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