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Open Mastery is an open framework for understanding, evaluating, and supporting learning in environments where AI is present.
(The formal definition of mastery is specified in the Open Mastery Standard.)
It is not a platform, a curriculum, or a product. It does not prescribe tools, workflows, or lesson plans. Instead, it defines a way of thinking about mastery, judgment, and evidence when explanation is abundant and execution is easy.
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At its core, Open Mastery is concerned with a single question:
How do we know when someone actually understands something?
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For a long time, education relied on friction to make understanding visible.
Writing took time. Calculation took effort. Producing an answer left traces of thinking along the way. Artifacts—essays, problem sets, exams—served as partial evidence of learning because they were difficult to produce without some degree of understanding.
AI changes that relationship.
When explanation, structure, and polish can be generated instantly, artifacts become less reliable as proof. A coherent output no longer guarantees coherent understanding. The gap between what can be produced and what is actually understood becomes harder to ignore.
This gap existed before AI. AI simply makes it visible.
(Related reflections appear in the essays **From Detection to Judgment* and When Friction Disappears.)*
Open Mastery does not attempt to restore friction through surveillance or restriction. It does not focus on detecting tool use or policing artifacts. Instead, it shifts attention toward judgment, explanation, transfer, and durability—the places where understanding still reveals itself.
In Open Mastery, mastery is not defined as performance, compliance, or completion.
Mastery is defined as sustained coherence of understanding across time, context, and variation.
(See Open Mastery Standard v1.5, Sections 3–6, for the formal definition and criteria.)
This means that someone who has mastered a concept can: