As generative AI becomes more embedded in higher education, we face a critical design choice: will these tools adapt to the full diversity of how students communicate, or will they enforce a narrow linguistic standard that advantages some learners and marginalizes others?
The default setting for most AI systems is Standard American English, a dialect associated with formal academic writing, news outlets, and professional communication. While there is value in helping students develop fluency across registers, there is also a cost when AI-powered learning tools treat this as the only legitimate form of expression. Students who speak regional dialects, use culturally specific phrasing, or come from communities with distinct linguistic patterns may need to frequently translate themselves to be understood by the technology meant to support them.
To foster belonging across our institutional systems and EdTech products, we must recognize that students have spent years internalizing the message that their language was informal, incorrect, or not academic enough. We must actively affirm the legitimacy of students' linguistic identities not just through tools, but through our daily interactions with them at every level of the institution. Linguistic diversity in learning technology is part of that.
The Case for Linguistic Diversity in Learning Technology
Localization is about improving access. It is about removing unnecessary barriers so students can focus on actual learning objectives. Consider the impact:
1. Localization fosters empathy
When students and faculty see their own local realities and others reflected in educational language, it builds trust in source content and enhances learning engagement.
2. Assessment and content mastery
Localizing language allows faculty and assessments to prioritize the subject of the course and grade learners on their understanding of the content more than their command of standard American English. This could directly impact retention and grade-based performance of underrepresented students in courses that do not center on English language mastery.
3. Representation reduces barriers
When students see their own language, rhythms, and realities reflected in learning experiences, the result isn’t just comfort, it is belonging. There is less pressure for students to translate themselves, mask, or code-switch to express themselves or feel heard.
4. Local knowledge is pedagogically rich
Regionally- or culturally-informed examples bring abstract concepts to life. Conversations about broadband access and digital learning will look different in rural Montana than in downtown Chicago. Discussing equity in Appalachia may invoke different histories than in urban California. We learn better when content begins with what we know and who we are, and expands from there.
5. AI can be a bridge or a barrier
Without intentional design, generative AI risks flattening language to a dominant norm. But with thoughtful prompting and structured outputs like JSON files, we can train systems to adapt to learners, rather than the other way around.
Design Choices Matter
A critical design choice presents opportunities. Because these technologies are flexible, we can choose to create systems that adapt to learners rather than requiring learners to adapt to systems. Without this kind of intentional intervention, we run the risk of generative AI defaulting to linguistic homogeneity—especially because these systems are trained primarily on formal written text that skews toward dominant dialects and cultural norms.
Creating learner-adaptive systems requires power-conscious design. Just as we've begun interrogating algorithmic bias, we must now examine linguistic bias in AI outputs. Who gets centered in the examples and explanations these tools generate? Whose ways of speaking are treated as legitimate versus corrected? What cultural knowledge is assumed as universal?
Practical Steps Forward
For higher education administrators evaluating or implementing AI-powered learning tools:
In procurement: Make linguistic inclusivity a criterion. Ask vendors how their systems handle language variation. Request demonstrations with diverse student personas and examine whether feedback mechanisms can distinguish between subject-matter understanding and adherence to specific language conventions.
In development: Invest in prompt engineering and output structuring that allows for regional and cultural adaptation. Test systems with students from diverse linguistic backgrounds and listen to their experiences. Create feedback loops that surface when language becomes a barrier.
In governance: Involve students and faculty from underrepresented communities in design and evaluation from the beginning. They can identify linguistic assumptions and barriers that dominant culture participants might never notice. Their knowledge isn't supplemental. It's foundational.
In professional development: Invest in training that challenges faculty and staff to examine implicit bias around Standard American English as the default measure of academic proficiency. EdTech tools are only as equitable as the people who use them, and if instructions still hold deficit views of learners’ home language, technology alone cannot close that gap.
The promise of AI in education is personalization at scale. But personalization that adapts to individual learning pace while enforcing linguistic homogeneity isn't truly personalized. It's efficient conformity. Localized language in AI-powered EdTech isn't a nice-to-have; it's a must-have. It's essential infrastructure for equitable learning.
For more information about how we’re building and testing AI tools to personalize language, click here.
.jpg)