Knowledge, existing as internal mental representations, is inherently difficult to measure directly. These dynamic representations, both conceptual and sensory, adapt to situational demands. Externalization, translating internal knowledge into observable artifacts, is crucial for assessment but is limited by knowledge’s implicit, context-dependent nature. External representations (e.g., texts, diagrams) approximate knowledge, requiring carefully designed tasks for meaningful expression. Traditional assessment, often focusing on deficits via error counting, contrasts with positive assessment, emphasizing knowledge artifact construction and providing richer insights into cognitive processes. This approach reveals knowledge aspects missed by conventional methods. Modern knowledge assessment combines adaptive testing, mental model elicitation, and performance-based assessments for improved reliability and validity. These methods address diverse knowledge types and the challenges of capturing complex representations. Effective knowledge, understanding, and assessment enhance learning, reasoning, and decision-making by bridging internal cognition and external behavior, ultimately improving our ability to evaluate and support cognitive performance across contexts.