Higher education has sometimes been criticized by composition and linguistics scholars for privileging a single, standardized form of English, often at the expense of the rich linguistic practices of people of color and other historically excluded communities1, 2, 3. When one version of English is dominant, other ways of speaking and writing can be framed as lacking, rather than as intellectual and cultural resources.
Language plays a central role in how many individuals share lived experiences, build a sense of belonging, and gain recognition within social groups4. Because languages and dialects grow out of specific social, cultural, and educational histories, they are always shaped by broader relations of power5. In this way, linguistic norms are not just about communication; they also determine whose knowledge is valued and whose voices are heard—including in academia6.
Hegemony of English in Canadian Higher Education
Today’s post-secondary institutions are shaped by complex linguistic ecologies arising from migration histories and ongoing internationalization. However, despite this increasing linguistic diversity on campus, monolingual, English-only ideologies continue to dominate educational discourse and practice in North America6. These ideologies systematically overlook and devalue students’ multilingual repertoires, limiting their full participation in academic life and marginalizing the significance of their linguistic backgrounds6.
The hegemony of English is therefore not merely a linguistic concern but a social and political issue that extends beyond individual interactions and classroom practices6. Because linguistic conformity is often associated with academic and professional success, it can reflect broader cultural values around achievement and competition. Developing critical awareness of English’s dominance is essential for exposing its consequences and for imagining more equitable approaches to language in higher education.
Linguistic Injustice in the Rise of GenAI
Concerns about linguistic hegemony have become more urgent with the rapid adoption of generative AI (GenAI) tools such as ChatGPT. While academic writing remains an essential skill, unguided use of GenAI to create text introduces new risks. Students may begin to see machine-generated language as more credible or authoritative than their own, which can discourage authentic expression and undermine confidence in their voices.
Most flagship GenAI tools—ChatGPT, Gemini, Claude—rely on predictive large language models (LLMs). LLMs are trained on vast textual datasets drawn from a range of sources, including books, transcriptions, academic publications, and web-based content. While this training allows models to easily imitate academic texts and other forms of writing, it also means that predictive models are prone to factual errors and hallucinations, and to reproducing gendered, racist, and ableist biases present in the data. Because these datasets skew towards dominant written traditions, they also often exclude marginalized perspectives and oral knowledge systems, including those of Indigenous communities and people of color7.
In addition, LLMs tend to flatten language variation, defaulting to so called standardized forms of English that limit creativity and constrain innovation. This aligns with GenAI’s tendency to “write white,” complicating claims that GenAI output is neutral or objective and highlighting its inability to represent diverse ways of meaning-making8.
Centering Student Voice Through a Linguistic Justice Lens
At its core, linguistic justice challenges hierarchies among different varieties of English. Dominant ideologies often portray languages as fixed and uniform, yet language use in practice is dynamic, relational, and informed by social context8. English is not a single, stable entity but rather as a constellation of intersecting and continually evolving varieties9. Framing English, or any language, as rigid or bounded obscures this reality and oversimplifies how language actually works in everyday life10.
In writing pedagogy, this understanding foregrounds student voice as central to linguistic justice. When academic conventions leave little room for students’ own language practices, they may feel increasing pressure to rely on GenAI tools—particularly when their own voices are regarded as incorrect or insufficient11. Supporting linguistic justice therefore involves rethinking not only how students write, but also how writing itself is valued and taught. Rather than responding to GenAI with stricter standardization, the arrival of GenAI invites us to reimagine academic writing in a way that affirms students’ linguistic resources, identities, and ways of knowing, positioning students and their voices at the heart of learning11.
Enacting Linguistic Justice in Practice
Linguistic justice can be supported through practical, reflective approaches:
Engage students in open conversations about GenAI—how it works, what kinds of language it produces, and why automated writing styles may be limiting or problematic, especially when GenAI use is permitted.
Encourage students to use their own language varieties and to reflect on the choices they make as writers.
Use varied forms of assessment and make expectations explicit to reduce reliance on unstated or implicit norms.
Reflect on personal language preferences and consider whether feedback is focused on correcting “errors” or supporting student growth.
Adopt an anti-deficit approach by describing the rhetorical impact of students’ language choices rather than labeling their ways of expression as “awkward” or “unnatural.”
1 Baker-Bell, A. (2020). Linguistic justice: Black language, literacy, identity and pedagogy. Routledge.
2 Flores, N., & Rosa, J. (2015). Undoing appropriateness: Raciolinguistic ideologies and language diversity in education. Harvard educational review, 85(2), 149-171.
3 Inoue, A. B. (2015). Antiracist writing assessment ecologies: Teaching and assessing writing for a socially just future. WAC Clearinghouse.
4 De Schutter, H., & Robichaud, D. (2015). Van Parijisian linguistic justice: Context, analysis and critiques. Critical Review of International Social and Political Philosophy, 18(2), 87–112.
5 Gee, J. (2015). Social linguistics and literacies: Ideology in discourses. Routledge.Tsuda, Y. (2010).
6 Speaking against the hegemony of English: Problems, ideologies, and solutions. In T. K. Nakayama & R. T. Halualani (Eds.), The handbook of critical intercultural communication (pp. 248-269). Blackwell Publishing Ltd.
7 Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?🦜. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623).
8 University of Michigan (n.d.). Linguistic Justice and GenAI. Sweetland Center for Writing. https://lsa.umich.edu/sweetland/instructors/guides-to-teaching-writing/linguistic-justice-genai.html
9 Canagarajah, S. (2023). Diversifying academic communication in anti-racist scholarship: The value of a translingual orientation. Ethnicities, 23(5), 779-798.
10 Pennycook, A. (2010). Language as a local practice. Routledge.
11 Thompson, F., & Pokhrel, L. H. (2024). GenAI: The impetus for linguistic justice once and for all. Literacy in Composition Studies, 11(2), 68-79.
This Creative Commons license lets others remix, tweak, and build upon our work non-commercially, as long as they credit us and indicate if changes were made. Use this citation format: Linguistic Justice in the Age of Gen AI. Centre for Teaching and Learning, Queen’s University