Empowering Education Through Culturally Responsive Teaching

Sandra Guzman Foster, Inaugural Dean of the Center for Teaching and Learning Innovation, Moravian University

Sandra Guzman Foster is the Inaugural Dean of the Center for Teaching and Learning Innovation at Moravian University. An experienced faculty leader with a Ph.D. in Educational Leadership and Policy Studies, she advances inclusive, culturally responsive teaching through faculty development, program evaluation and evidence-based innovation. Her work centers on equity, belonging and meaningful learning across digital, hybrid and campus environments.

Dr. Maria Chen stares at her screen, reviewing AI-generated discussion prompts for her online sociology course. The questions are grammatically perfect, covering all her learning objectives. She reviews one of the AI-suggested prompts: “Discuss how social norms influence behavior.” Something seems off. She reviews it again. It’s accurate, but generic and stripped of the cultural specificity that makes sociology come alive. She thinks of her first-generation college students from over twenty countries, who bring rich cultural perspectives to the course. Will this prompt invite their voices, or silence them? Maria realizes she’s at a crossroads familiar to educators everywhere: how do we harness AI’s efficiency without sacrificing the cultural responsiveness that makes learning transformative?

This scenario reveals AI’s dual potential: it can personalize learning or risk cultural homogenization. As AI reshapes online education, culturally responsive teaching (CRT) becomes more critical than ever. The question is: how can we harness AI as a tool for educational fairness while addressing challenges like Maria’s? When educators ground academic content in students’ lived experiences, prior knowledge and cultural frames of reference, learning becomes more meaningful. Students engage more deeply and learn more effectively, leading to stronger classroom outcomes (Gay, 2000).

Dr. Chen recognizes a critical tension between algorithmic efficiency and cultural authenticity. AI-enhanced education can systematically disadvantage students from non-dominant cultures. AI systems trained primarily on Standard Academic English struggle to recognize the validity of African American Vernacular English (AAVE), code-switching and multilingual expression, often flagging students’ authentic cultural communication as errors. Additionally, assessment systems privilege Western conventions like sequential, thesis-driven reasoning over Indigenous storytelling and collectivist approaches, which are authentic and valid ways of knowing. By prioritizing algorithmic efficiency, these systems embed cultural hierarchies into educational technology, devaluing diverse ways of knowing.

Before integrating AI tools into their classrooms, educators must interrogate and audit them for cultural biases. Ask questions like, “Whose voices are represented? What assumptions are embedded? Whose knowledge is being excluded?” If using AI to generate lessons or classroom activities, educators must critically evaluate them for cultural inclusivity and accessibility. We must move beyond a superficial understanding of AI tools and cultivate a deep AI literacy that is inherently culturally responsive. This means critically examining existing AI literacy frameworks with attention to educational fairness, ensuring they address the diverse backgrounds, experiences and needs of our students. The key is to become cultural interpreters and critical guides, ensuring that technology serves humanity, not the reverse.

"How do we harness AI’s efficiency without sacrificing the cultural responsiveness that makes learning transformative?"

Dr. Chen’s challenge with generic AI prompts underscores a fundamental culturally responsive principle: education must honor multiple ways of knowing. Instead of relying solely on the AI-generated prompt, Dr. Chen could offer students a choice of response formats that align with their cultural backgrounds and learning preferences. For example, in addition to her prompt, “Discuss how social norms influence behavior,” she can add, “You can respond in a written post, a short video, or an audio recording. Feel free to use examples from your own life, current events, or other sources that resonate with you.” This approach honors students’ voices while cultivating learner agency through flexible engagement with course material.

Additionally, she could revise her initial post to: “How do the social norms in your community or cultural background shape individual behavior? Provide specific examples from your own experiences or observations.” This invites students to share their unique perspectives, making the discussion more relevant and engaging while fostering a critical perspective on cultural issues. By designing for multiple ways of knowing, Dr. Chen transforms the discussion from a potentially homogenizing experience into a culturally enriching one.

Educators must teach students to question AI outputs, not passively consume them. Dr. Chen could create space for examining AI’s societal implications, building critical AI literacy alongside her students. Critical AI literacy extends beyond tool proficiency to encompass understanding, applying and assessing AI operations, uses and outputs. For example, Dr. Chen could facilitate a classroom activity where students rewrite the AI-generated prompts to be more culturally responsive, incorporating diverse perspectives, using more inclusive language and connecting the prompts to students’ lived experiences. Students could analyze the original prompt, identify its limitations and collaboratively brainstorm alternatives that are more relevant and engaging for their diverse classroom community.

By building critical AI literacy together, Dr. Chen empowers her students to become active agents in their own learning, questioning the assumptions and biases embedded in AI systems and using AI tools in ways that align with their values and advance inclusive access to meaningful education.

As AI reshapes online education, culturally responsive teaching becomes essential for ensuring fair and inclusive learning experiences. Through Dr. Chen’s classroom scenario, this article demonstrates how AI systems can perpetuate cultural biases while simultaneously offering opportunities for more inclusive pedagogy. By centering educator agency, educators are able to transform AI from a potential source of bias into a tool for culturally responsive, inclusive digital learning.

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