Not Cheating, Just Changing: Ethnomathematics in the Age of AI
Mathematics as a Cultural Practice
Ubiratan D’Ambrosio’s seminal definition of ethnomathematics invites us to expand our understanding of what qualifies as legitimate mathematical knowledge. He describes ethnomathematics as “the mathematics which is practiced among identifiable cultural groups…[whose] identity depends largely on focuses of interest, on motivation, and on certain codes and jargons which do not belong to the realm of academic mathematics” (D’Ambrosio, 1985). In their analysis of his work, Powell and Frankenstein (1997) go further, emphasizing that even academic mathematicians have their own ethnomathematics, and that D’Ambrosio “views mathematical knowledge as dynamic and the result of human activity—not static and ordained.”
When I first encountered these ideas, they completely reoriented how I thought about mathematics, teaching, and leadership. They didn’t just expand my academic lens—they ignited something in me. As someone deeply committed to instructional leadership and systems design in STEM education, I found in ethnomathematics a powerful framework that spoke to both cultural identity and scientific rigor. These theories pushed me to imagine math classrooms not as sites of sorting and exclusion, but as spaces of inquiry, purpose, and belonging. And they deepened my resolve to empower learners—especially those from historically excluded communities—to use STEM tools to build sustainable futures and design systems that support a just society.
These foundational ideas set the stage for more contemporary frameworks from scholars like Rochelle Gutiérrez, Danny Bernard Martin, and Gloria Ladson-Billings, who each build upon and challenge institutionalized conceptions of mathematics. Their work collectively pushes the field toward rehumanizing math, making it culturally and politically responsive, and grounding it in liberatory pedagogies.
Emerging technologies like generative AI introduce a new layer to these frameworks. These tools hold promise for expanding access and supporting collaborative, inquiry-based learning. But this promise is conditional: we must be intentional about how students engage with them. Are these technologies deepening mathematical thinking? Are they helping students analyze systems and craft meaningful solutions? If not, we risk reinforcing the same inequities these scholars challenge.
These ideas have never been more urgent.
The Technological Shift Shaping Mathematical Practice
We are currently living through a massive cultural and technological shift. Youth culture is increasingly mediated by artificial intelligence, predictive algorithms, and ubiquitous computational systems. Yet, the mathematics we teach in most K–12 schools still clings to an industrial-era logic: static, context-independent, and artificially decontextualized from the technological environments where most mathematical activity now occurs. In sociological terms, technology is not just the hardware or software we use—it’s the ever-evolving set of practices that transform how we think, produce, and relate to one another. If culture is shaped by how we live, and technology changes how we live, then cultural shifts driven by technology necessarily give rise to new forms of Ethnomathematics.
If culture is shaped by how we live, and technology changes how we live, then cultural shifts driven by technology necessarily give rise to new forms of Ethnomathematics.
Generative AI and the Challenge to Academic Mathematics
Generative AI is prompting us to rethink what counts as “academic mathematics.” It doesn’t replace mathematical thinking—but it does expose the limits of traditional definitions of rigor. AI pushes us to reconsider what it means to understand, model, and decide. While some educators continue to uphold math as a universal language grounded in abstraction and procedure, others see a chance to expand how we define and value mathematical reasoning.
In classrooms, this shift is already underway. Tools like ChatGPT can now complete problem sets or simulate complex solutions. The concern is often framed as cheating—but the deeper issue may be our failure to teach students how to think critically about the algorithmic environments they already inhabit. Without this, students may struggle to recognize when a quantitative result feels off, or how to investigate it.
Reimagining Classroom Mathematics as Design and Interpretation
For nearly a decade, I’ve designed tasks that center students as interpreters and designers of quantitative tools. Instead of treating technology as a cheat code, we frame it as an ethnomathematical context: an authentic part of the world that shapes and is shaped by mathematical practices. Students use generative AI and data dashboards not to bypass learning, but to explore which concepts and algorithms are most appropriate to their inquiries, and to contextualize and annotate their results.
What We Learned from Students at Howard University
In our work at Howard University, we saw this in action. Several undergraduate students, many of whom identified as non-STEM majors, used generative AI to support data visualizations and adapt code related to their social science learning activities and research topics. What often began as a technical challenge—such as cleaning a dataset or displaying a graph—quickly evolved into deeper mathematical inquiry. Students engaged in rich conversations about data structure, modeling decisions, and the ethical implications of their visual representations. These discussions often led to revisions not just in code, but in research questions, comparative frames, and contextual interpretations. Through peer feedback and collaborative troubleshooting, students developed confidence in their ability to interpret quantitative tools—and began to see themselves as capable of shaping how data is used to tell meaningful stories.
What Counts as Math in the Age of AI
In both secondary math classrooms and undergraduate settings—especially with students who do not identify as “math people"—this approach has deepened engagement and understanding. Students learn to decode and adapt models, assess the implications of their assumptions, and articulate how tools must be tuned for their intended purpose. This is not a rejection of rigor—it’s a re-centering of relevance. It’s mathematics as reasoning in context, mathematics as design, mathematics as collective sense-making.
What we’re seeing in these classrooms is not just a change in tools. It’s a change in what counts as math. It’s the emergence of a new Ethnomathematics rooted in AI-enhanced collaboration, visual modeling, contextual decision-making, and ethical inquiry. And if we are serious about educational equity, we must embrace this shift.
Toward a New Vision for Math Education
As AI becomes more common in classrooms, students need more than just exposure—they need agency. They shouldn’t just use AI to get answers; they should learn how to question, verify, and think with it. This means reimagining what math learning looks like.
Understanding how tools work: Students should explore how digital tools, especially AI, make decisions—what data they use, how they calculate, and where they might go wrong. Learning to double-check AI’s answers helps build strong critical thinking skills.
Using real-world tools: From interactive simulations to simple coding platforms, students should have access to age-appropriate tools that reflect how math is used outside the classroom. These experiences show that math isn’t just about right answers—it’s about exploring and understanding.
Thinking in systems: Math education should help students see the bigger picture—how models are built, what assumptions they’re based on, and how we use them to make decisions. This kind of thinking helps students better understand both math and the world around them.
To redesign math and STEM classrooms for today's world, we need an interdisciplinary approach that blends math with social sciences, natural sciences, and data science practices. This means creating more opportunities for students to engage in meaningful, real-world investigations—both inside and outside of core math courses—using tools that reflect how math is applied in professional contexts.
These experiences, especially those involving annotated modeling, help students build what I see as 'AI-proof' math learning: skills that go beyond answering questions to interpreting, designing, and reasoning through complex problems. They foster fluency with modern tools while reinforcing foundational concepts through intentional recall.
Reimagining math education isn’t just about introducing new technologies—it’s about asking better questions. Questions that invite students to interpret, critique, and collaborate using mathematics that mirrors the world they live in. AI literacy must be treated as part of mathematical literacy, with emphasis on modeling, annotation, and metacognitive thinking.
By moving away from procedural gatekeeping and expanding our understanding of what counts as math, we can create a more inclusive and sustainable future—one where mathematical power is accessible to all. In this light, ethnomathematics asks not that we abandon standards, but that we examine whose standards have been prioritized, and whether they meet the needs of today’s learners.
References
D’Ambrosio, U. (1985). "That's not mathematics!" and its place in the history and pedagogy of mathematics. For the Learning of Mathematics, 5(1), 44–48.
Powell, A. B., & Frankenstein, M. (1997). Ethnomathematics: Challenging Eurocentrism in mathematics education. SUNY Press. Section I: Ethnomathematical Knowledge, pp. 5–9.
Gutiérrez, R. (2013). The sociopolitical turn in mathematics education. Journal for Research in Mathematics Education, 44(1), 37–68.
Gutiérrez, R. (2017). Living Mathematx: Towards a vision for the future. Philosophy of Mathematics Education Journal, 32.
Ladson-Billings, G. (1995). Toward a theory of culturally relevant pedagogy. American Educational Research Journal, 32(3), 465–491.
Ladson-Billings, G. (2006). From the achievement gap to the education debt: Understanding achievement in U.S. schools. Educational Researcher, 35(7), 3–12.
Martin, D. B. (2009). Mathematics learning and participation in African American contexts: The co-construction of identity in two intersecting realms of experience. Charlotte, NC: Information Age Publishing.