GenAI: The Impetus for Linguistic Justice Once and For All
Faith Thompson—Salisbury University
Lauren Hatch Pokhrel—Salisbury University
Keywords: linguistic justice; voice; style; generative AI; practitioner reflection; student writing
ABSTRACT
Conversations around generative AI (GenAI) and linguistic justice are dominating scholarly conversations in composition studies, and yet little work looks at the two together. We argue that the rise of A.I. can serve as a kairotic moment for enacting linguistic justice by returning to expressivist approaches to student writing. We share experiments working with GenAI platforms in attempts to produce diverse voices in writing and then offer our own experiences as writing instructors centering student voice in instruction.
In recent years, the use of generative AI platforms such as ChatGPT has grown exponentially in college writing classrooms and programs. This rise has occurred against a backdrop of critical scholarship in composition studies calling for a revamping of traditional academic writing that perpetuates the notion of a single, standard, and correct form of academic English that mimics white, middle-class languaging practices. While the two phenomena may seem unrelated, we believe that, taken together, they create a kairotic moment for the pursuit of linguistic justice. Manyc ollege instructors and scholars already recognize that GenAI cannot replicate the brilliance of students’ own critical thinking, however it is also important to acknowledge that GenAI cannot replicate the authentic voice of student writers. Thus, we argue that the rise of GenAI necessitates a reimagining of academic writing conventions that place students, and their voices, as the focus through the lens of linguistic justice.
CONTENTS
On its website, the University of Michigan’s Sweetland Center for Writing asks, “Where is the GenAI for Black English?”1 As writing center tutors and writing instructors whose research interests reside in linguistic justice pedagogies, we wondered the same thing. We knew that Black English (BE) has been framed as a “distorted dialect [of] minstrel-show performers,” (McWhorter 21), as a “mutant” of old and middle English (Smith), and as “ghetto” (Baker-Bell; Kirkland). We also knew that generative AI tools were known for problematic and racist “behaviors” (Holmes-Iverson). With these issues in mind, we decided to experiment with using ChatGPT, a generative AI platform where you input a prompt and it generates, or outputs, a response. We asked ChatGPT to generate a recipe in BE. A copy of its output, unedited, appears in Figure 1.
Granny's Soulful Mac and Cheese
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Fig. 1. A Mac and Cheese Recipe Created by ChatGPT Writing in BE.
Now, ignoring the fact that ChatGPT chose to make this recipe a “Soulful Mac and Cheese,” doesn’t this read like the racist stereotype of Chef from South Park? It sounds like someone making fun of BE more than it does someone speaking it, demonstrating the stereotypes that frame GenAI’s understanding of BE, and is reminiscent of racist minstrel shows.
Our experiment demonstrates that even when asked expressly not to do so, ChatGPT reflects “Western, male-dominated, white,2 middle class understanding and expectations” (Sharma) about language use, ideologies, and the people who practice languages outside of white mainstream English. Graham Stowe explains that the algorithm can only reflect its creators and the dominant hegemonic linguistic systems and hierarchy embedded in our society today. This suggests that even when we attempt to counter ChatGPT’s inclination towards white mainstream English3(Lester 23; Stowe; “Linguistic Justice”), racial biases towards marginalized language users are still present. Essentially, “the technology excludes the possibility for . . . dialects to be incorporated into the meaning-making process” (“Linguistic Justice”), thereby limiting the ability of students who speak a non-standardized English to have meaningful linguistic representation in GenAI.
Our experiment is not the only one to demonstrate this racial bias for white mainstream English. According to Jeremy Hsu, researchers at the Allen Institute for AI input text written in Black English and white mainstream English and asked ChatGPT to characterize the authors of the text. They used ChatGPT-4, a model that supposedly has undergone antiracist “training” for its outputs. Despite this training, authors of inputs using Black English were characterized as “suspicious,” “aggressive,” “loud,” “rude,” and “ignorant,” demonstrating negative stereotypes associated with Black English (Hsu). When the researchers also asked the AI to match the speakers of Black English and white mainstream English with specific jobs, Black English authors’ inputs were associated with unemployment, jobs not requiring university degrees, or music and entertainment (Hsu). The Allen Institute for AI completed other experiments that further demonstrated the depths of generative AI’s racism even after antiracist training.
Nevertheless, because academic writing conventions primarily favor white mainstream English, multilingual and multidialectal students may be more compelled to turn to AI for their papers in order to approximate such academic writing conventions without fully understanding AI’s adherence to white language supremacy. It is for these reasons that we as writing educators must respond to this particular moment, which necessitates a re-examination of academic writing that enacts linguistic justice.
GenAI, and largely the fear of it for some, has swept colleges across the country. Kalley Huang explains how many professors are turning to in-class writing, hand-written essays, use of browsers that monitor student activity, or extensive explanations of revisions in an effort to avoid student plagiarism through AI. Some faculty, according to a report in the New York Times, might even be foregoing essays in general (Huang). On the other hand, many professors are finding ways to include GenAI in the classroom, even so much as requiring it. Both alarmist and inclusive approaches demonstrate that all stakeholders are scratching their heads to find the most effective responses to GenAI in ways that address learning objectives, the epistemological nature of writing, and student agency. A rehaul of the academic writing conventions as they have been traditionally practiced is the only way towards both enacting linguistic justice and continuing to value writing’s role in student meaning-making. Dani Lester argues that:
[I]f there is a potential advantage to GenAI’s proficiency and adherence to white mainstream American English (and its deleterious effect on voice and language), it’s that GenAI has made material and visible the otherwise slippery linguistic slope toward white patriarchy. This problem creates an opportunity to implement more innovative and radical practices to address systemic injustice, building off current practices regarding technology literacy and encouraging ownership. (23)
As graduate students, writing center tutors, and educators committed to linguistic justice, we do not hope for more sophisticated AI tools that attempt to mimic non-standardized English varieties as Julia Nee, Genevieve Macfarlane Smith, Alicia Sheares, and Ishita Rustagi (4) argue for but, rather, for academic writing conventions to reprioritize the use of style, rhetorical choices, and the integration of one’s own lived experiences. We argue that GenAI users seeking to make AI more linguistically diverse are not working towards linguistic justice but possibly reifying harmful languaging stereotypes. GenAI, in fact, cannot answer the call of linguistic justice precisely because it fails at language diversity; students’ voices cannot be effectively and meaningfully mimicked. Understanding the racist nature of language generated by AI then, this moment compels us to call for a disruption of academic writing conventions that prioritize linguistic justice through writing tasks that value student voice more explicitly.
Linguistic justice refers to the dismantling of oppressive language ideologies and white linguistic supremacy, according to April Baker-Bell (7). It explicitly centers BE and the experiences of BE users. Pawlowski theorizes several tenets of linguistic justice:
Centering writers’ voice in writing instruction has been suggested as a strategy in pursuit of linguistic justice for users of BE (Thompson as well as all multilingual students (Proctor, Silverman, and Jones). By voice, we refer to language usage, the inclusion of lived experiences, and rhetorical choices as determined by the writer. Much academic writing within college contexts dismisses the subjectivity of the writer, while also implicitly or explicitly requiring white mainstream English. In some extreme cases, these conventions have turned academic writing into a dehumanized and disembodied process and mode, (and perhaps AI generated writing is a manifestation of this dehumanized and disembodied writing). A reimagined approach to writing “places the writer at the center . . . valu[ing] the writer’s imaginative, psychological, social, and spiritual development” (Burnham and Powell 113). These approaches to writing value students holistically and reinforce the notion understand that we cannot separate a student from their writing, just as students cannot separate abstract concepts in their classrooms from their own lived experiences and realities. In this way, teachers can enact bell hooks’s call for “engaged pedagogy” that “sees [students] as whole beings with complex lives and experiences” instead of just “compartmentalized bits of knowledge” (15). By faculty more explicitly inviting the student voice into students’ academic writing, students can enact more agency over their own languaging and rhetorical choices as well as humanize their writing through the inclusion of their own subjectivity. Additionally, valuing student language practices and their wholeness within student writing might, in many ways, mitigate student impulses to use GenAI for their writing assignments and aspires to pursue linguistic justice.
Below, we offer our thoughts as writing center professionals and writing instructors attempting to center linguistic justice principles in our writing classrooms during this GenAI era.
Practitioner Reflection: (Faith)
In my experiences teaching first-year composition, teaching and emphasizing writerly voice served two purposes. First, in terms of linguistic justice, I wrestled every day with how best to meet the needs of my majority English-learning multilingual classroom as a white, monolingual English-speaking woman who is often understood to be using standardized English. There was tension between my values of not teaching them assimilationist standardized English, but still teaching them the English they needed to be successful in their subsequent classes. This was not a fear of some imaginative grammar nazi professor teaching my students, but rather me coming to terms with the reality of my role in a college ecosystem—my class is designed to prepare them for other classes. I turned to voice as a way to navigate this.
I taught my students that their writing comes from them, that their identity is intrinsically tied to their writing, and that their writing can and should sound like them. In particular, I honed in on word choice and vocabulary. I banished the thesaurus—no more digging for big words—and I introduced code meshing. Is Spanish the best way to say this idea? Then say it in Spanish and explain the connection in English. I taught them to think about audience and purpose when writing, and whether they wanted to use vernacular to communicate with this audience and for what purpose. We learned about tone, how to sound less casual while maintaining one’s own style. Focusing on voice allowed me to teach the skills of writing and rhetoric, not just prescriptivist formulas and grammatical rules.
A second purpose voice served in my class was to get to know my students and their writing. With over 50 students in my first-year composition course, and a dense curriculum to get through in 15 short weeks, the main way I got to know my students was through their writing. My class started with a personal narrative allowing me to get a sense of my students’ styles and voices. By the third essay, I could tell my students’ writing apart. Bianca,5 for example, was a former translator whose writing always sounded very technical, and José was passionate for his topic and tended to be very conversational in his style. I learned a lot about the personalities of my students and how to best work with them through their writing.
This purpose was twofold. Getting to know my students’ voice also means I know when something is written in that voice. Early in my teaching career, I had a conversation with another teacher about a student whom she suspected had plagiarized late in the semester. She suspected the plagiarism because he’d been writing for her all semester and it “didn’t sound like him.” In other words, his voice and style were not present in this essay. This would prove to be my litmus test in my classroom as well. When student writing didn’t “sound like” the student I’d gotten to know, I approached them to discuss how they wrote the paper. Of course, it wasn’t plagiarism every time. However, my emphasis on voice allowed me to both catch and address plagiarism when it did happen and to intervene when students were using strategies that erased their unique voices.
In my experience, GenAI is the easiest non-student voice to catch. It simply cannot mimic authentic voice and reads robotically. It isn’t capable of code-meshing like my students are. It isn’t capable of expressing tone or finding that passion for the topic José had. If we’re concerned about plagiarism, perhaps allowing students to foster and develop their own unique, authentic voice and style is a way forward.
Practitioner Reflection: (Lauren)
Being a white educator in a first-year composition classroom at a Historically Black College and University (HBCU), I experience the complexities and tensions of cultivating linguistic justice at a time when student GenAI is common. Every semester, I include a unit on critical language awareness (CLA) in the standardized English 101 curriculum, with the aim of fostering more critical understandings of hegemonic language practices that reinforce white mainstream English and permeate academic writing. At the end of the unit, students produce a specific argument on language ideologies within educational contexts using the unit’s texts and, if they prefer, their own lived experiences as support. Each semester, a majority of essays take some sort of critical stances that either demonstrate the many issues of standardized language practices, such as linguistic racism against Black English (Baker-Bell) or r. Sometimes student essays argue for validating and embracing marginalized languages in academic settings as explained by Vershawn Ashanti Young. Throughout the unit, students typically demonstrate an evolution from internalized dominant language ideologies, and sometimes internalized anti-Black linguistic racism (Baker-Bell), to more critical understandings, most exemplified in their final essays. In their final semester reflections, students overwhelmingly say that this unit resonated with, interested, and even surprised them the most.
But I understand and experience the many complexities and “perpetual ‘buts’” (Howell, Navickas, Shapiro, Shapiro, and Watson.) of engaging in linguistic justice as a white, monolingual speaker in an authoritative position over multilingual and multidialectal students of color. I have grappled to varying degrees with students making critical arguments around languaging, reflecting on their own uses of Englishes and other languages but seeming to want to attempt to maintain a standardized English form in their actual writing. Some scholars argue that translanguaging, or code-meshing, is a naturally occurring phenomena whether it appears visibly or invisibly in writing (Guerra); while others encourage the use of visible code-meshing in writing (Young). Instructors assessing student writing in hopes of seeing the manifestations of their teaching efforts run the risk of engaging in linguistic tourism, as Paul Kei Matsuda suggests, which flattens consistent and prevalent hierarchies around language varieties.
Further, encouraging students to visibly code-mesh in their writing creates a new form of multiculturalism that further dismisses the systemic and material impacts of language ideologies and ironically reinforces monolingual logic through difference, according to Bruce Horner and Sara P. Alvarez. It is for reasons such as this that some instructors reject any critical attention to trans/languaging altogether. Merely inviting students to translanguage in their writing in a composition classroom is little help to the instructor who somehow assesses that writing. Expanding academic literacy to more explicitly prioritize student voice also runs the risk of tensions around authenticity and performance (Royster), particularly, in my case, for the white gaze resulting in reifying and centering whiteness through the guise of authentic voice that in many ways mirrors the stereotyped language of our initial experiment. Aspiring to enact linguistic justice within my own writing classroom has prompted me to reflect: How do assignment requirements intentionally or unintentionally demand white mainstream English? And what does it mean for a multilingual student to code-mesh in their writing, only to be assessed by a white, monolingual instructor?
While not all students use programs like ChatGPT, there does seem to be a general misunderstanding of many of my students in thinking that these bots can produce better writing than they can. And given the certain assignment descriptions I see as a graduate writing center tutor that put a high amount of weight on “style, grammar, and mechanics,” perhaps these students aren’t wrong. In my own teaching and tutoring experiences, I see the urgent need to embed linguistic justice into assignment prompts and rubrics, and to do so rather quickly as GenAI will only become increasingly more sophisticated and prevalent––and to what end? I value the language practices, lived experiences, values, and epistemologies of the human bodies that sit in my classroom. Why would I not then want to understand my students more through their own writing, particularly in a writing workshop course? It is because of my privileged position as a white, educated instructor whose language practices are valued in academia, not in spite of my positioning, that I wrestle with these tensions—perhaps failing repeatedly, but continuously trying to enact linguistic justice within my classroom. It is for these reasons that educators must return to the importance of student voice in writing as a pedagogy of linguistic justice during these GenAI-saturated times.
As stated in our reflections, not suggesting a stronger focus on voice through a linguistic justice approach is not without its complications and nuances. Given that most academic writing privileges white mainstream English, it is not surprising that multilingual and multidialectal students, who may already be academically disadvantaged, would be inclined to use it in their writing. At the same time, research indicates that such students are disproportionately penalized for using GenAI (Addy, Kang, and Laquintano, Dietrich 4; McDonald, Johri, Ali, and Hingle 17), especially in terms of accusations of plagiarism. Further, going by a rule of thumb such as “does it sound like the student” can be a harmful practice if based on assumptions of students' language. This should never be a method used to penalize a student, but rather to open a conversation about concerns, and should only be utilized after several writing samples are collected.
Additionally, we understand that encouraging more representation of student voice within academic writing also is complicated. In some cases, as mentioned in Author 2’s reflection, students are asked to perform authenticity and then assessed by an instructor whether or not they performed it accurately (Royster). Rather than framing these tensions of GenAI usage in opposition to our argument, we see them as the exigence. That is, that academic writing conventions that reject or dismiss student voice, making it easier for students to feel compelled to use GenAI, further reveal the need to realize linguistic justice in writing contexts. If we want to humanize our students and help them make meaningful connections from their classes to their worlds, we must re-examine academic writing conventions to finally adopt a linguistic justice framework through valuing student language practices and lived experiences.
We share our stories here to encourage reflection on what the goal of writing instruction can and should be in a world with constantly evolving technology-assisted writing practices. We hope we’ve offered some insight into the importance of centering student voice and style; it is our belief that responses to GenAI in writing classrooms should prioritize principles of linguistic justice.
In this offering, we also wish to acknowledge our privilege as white women. We do not ourselves experience the linguistic racism that many people of color and BE speakers do. In our roles as writing tutors and instructors, we are positioned as academic gatekeepers of white mainstream English, something Asao B. Inoue argues is inherent in American academic writing education even as one may attempt to disrupt it (25). This positioning, however, has allowed us to witness the marginalization that linguistic racism perpetuates on BE language users, as well as on speakers and writers of other marginalized and racialized dialects of English, in the college writing classroom. We recognize that as writing educators, we need to be vigilant and constant in our unlearning of a bias towards white mainstream English.
For readers who would like to expand on this work, we suggest another thought experiment: ask ChatGPT to write a recipe in Spanglish, Appalachian English, Southern English, or any dialect of your choice and consider the results with your students. Such experiments are not baseless activities, but can be enacted in workshops to, at the very least, position GenAI as what it is: the AI equivalent of a white, Western man. Given diverse student populations, this is an entry point into the necessary and larger conversation regarding a rehaul of academic writing conventions. We further suggest continued, critical research into the antiracist training of generative AI platforms like ChatGPT to hold them accountable for linguistic racism.
There are myriad ways for readers to center student voice and expression in their classrooms as a way to enact linguistic justice. A. Suresh Canagarajah and Thir Budhathoki each encourage the creation of writing assignments that allow students to draw on their own knowledge and background; for example literacy narratives or autobiographies are recognized as “motivating, accessible, and authentic for their students” (Budhathoki 46). Literacy narratives are an opportunity for translingualism, in which academic literacy is seen beyond the bounds of white mainstream English language norms. Translingualism, Budhathoki argues, is not just limited to second language learners or multilingual writers, but can be inclusive of US domestic English monolingual students who speak marginalized dialects. We can also offer students more low-stakes assignments (Canagarajah) as they continue to practice navigating academic writing conventions while developing their writing voice, and students should be exposed to a range of diverse texts that disrupt white mainstream English writing conventions (Seltzer).
Ultimately, much more research and theorizing is needed to fully understand how generative AI could be a catalyst for more linguistically just college writing education. A question arising from our work that scholars might explore is how can a focus on style and voice in the college writing classroom alter students’ reliance on and relationship with generative AI writing?
1We have chosen to use the term Black English (BE) to refer to what is also known as African American Vernacular English (AAVE) or African American Language (AAL). While different scholars have different reasons for the terminology selected, we are choosing this term to validate BE as a variety of English while also recognizing that there is ongoing debate regarding whether it is a dialect or language. We also wish to emphasize that BE varies across context and speakers and is not just a single “dialect.”
2 In this paper, we will be capitalizing Black but not white in recognition of the nature of race as a social construct rather than a fact. Black, however, represents not just race but also culture and community (Dumas). This capitalization is also standard Associated Press practice (Bauder).
3 White mainstream English is used to refer to the dialect of English also known as standardized American English. We use this term to emphasize the racial nature of dialects in the US context and in the tradition of scholars like Baker-Bell.
4See Pawloski.
5 We have used pseudyonyms for all students.
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