In this episode of The Cultural Context of Knowledge, host Donald Easton Brooks challenges one of the most common assumptions in education: that STEM is neutral. If we say “math is objective” and “data speaks for itself,” what gets hidden is the cultural design of many STEM classrooms—speed, linear reasoning, abstraction-first instruction, individual performance, and language-heavy explanations. These norms are not universal; they are traditions that have been normalized as intelligence.

This is not a call to lower standards or add “culture” as an extra layer. It is a redesign challenge: how do we build STEM learning where rigor is not confused with restriction, and where students don’t have to earn belonging before they can learn? You’ll leave with concrete redesign moves—starting with real-world systems before formal notation, widening how students can show reasoning, and rethinking assessments that measure familiarity and speed more than understanding.

In this episode of The Cultural Context of Knowledge, host Donald Easton-Brooks asks a foundational question most schools rarely name: What counts as knowledge—and who gets to decide? Moving beyond the myth that curriculum and assessment are neutral, this episode examines how knowledge is shaped by history, culture, and power, and how “standard” definitions of intelligence can privilege some ways of knowing while marginalizing others.

You’ll be introduced to key ideas, including epistemology, master narratives, sociocultural learning theory, and Funds of Knowledge, with clear connections to everyday classroom practice. The episode also explores Community Cultural Wealth and the consequences of misrecognition—the hidden “tax” students pay when they must constantly translate their identities and reasoning to be seen as capable. The closing challenge is practical and urgent: redesign learning so schools stop asking, “How do we fix students?” and start asking, “How do our systems fail to recognize what students already know?” This is an episode for educators, leaders, and learners committed to rigor, equity, and meaningful learning.

In this episode of Cultural Context of Knowledge, Dr. Donald Easton-Brooks compares Episode 1's use of NotebookLM with his narrated work to examine the central truth about learning: genuine learning is a productive struggle that moves us from uncertainty to understanding through a sequence of steps. Using Bloom’s Taxonomy as a frame, the episode explains why foundational concepts must be built before higher-order analysis and creation are possible, and why AI tools like ChatGPT and Gemini can become harmful when they encourage “sequence-skipping.” The conversation then extends beyond process into purpose, connecting AI-supported learning to the cultural context of knowledge: how history, lived experience, dominant narratives, and “whose knowledge counts” shape what we understand and how we interpret it. Educators and learners will leave with practical guidance for using AI as a scaffold that strengthens learning, supports inquiry, and remains culturally responsive, rather than replacing the struggle that makes understanding durable.